1. Deep Learning — Ian Goodfellow, Yoshua Bengio, Aaron Courville (HTML; no signup). This seminal MIT Press text provides a sweeping treatment of deep learning theory and practice, covering everything from linear algebra and probability theory to convolutional and generative models. The authors note that the online version is complete and will remain freely accessible. The book uses an HTML format rather than a downloadable PDF because the MIT Press contract forbids easy‑to‑copy electronic formats.
2. Understanding Deep Learning — Simon J. D. Prince (PDF/HTML; no signup). Prince’s 2024 textbook strikes a pragmatic balance between theory and practice, distilling the most important ideas in deep learning into an intuitive narrative. The free computer books entry lists the ebook as Creative‑Commons licensed and highlights that it explains Python implementations for tasks like natural‑language processing and face recognition
3. An Introduction to Statistical Learning — Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani (PDF; no signup). Often abbreviated ISLR, this classic introduces regression, classification, resampling, regularization, support‑vector machines and more. The authors explain that the book provides a broad and less technical treatment of statistical learning concepts, and the site offers free PDF downloads of the first and second editions as well as the new Python edition
4. The Elements of Statistical Learning — Trevor Hastie, Robert Tibshirani, Jerome Friedman (PDF; no signup). A foundational text for researchers, it delves into advanced topics such as boosting, support‑vector machines and graphical models. The authors have made the entire book available as a free PDF, and many graduate courses reference its rigorous treatment of machine‑learning theory.
5. Mathematics for Machine Learning — Marc Peter Deisenroth, A. Aldo Faisal, Cheng Soon Ong (HTML/PDF; no signup). This book provides the linear algebra, calculus and probability foundations required to understand modern machine‑learning algorithms. The authors released the PDF and HTML versions under a permissive license, making it easy to read online or download for personal study.
6. Dive into Deep Learning — Aston Zhang, Zachary C. Lipton, Mu Li, Alex J. Smola et al. (HTML/PDF/Jupyter notebooks; no signup). D2L is an interactive, open‑source book built with Jupyter notebooks; it covers deep learning fundamentals with code examples in multiple frameworks and is updated continually by the community. The HTML version is freely accessible and can be converted to PDF or run locally.
7. Reinforcement Learning: An Introduction (2nd ed. draft) — Richard S. Sutton, Andrew G. Barto (HTML/PDF; no signup). This book introduces reinforcement learning concepts from dynamic programming to policy‑gradient methods. The authors have posted the entire second‑edition draft online, emphasizing that it is freely available for educators and students.
8. Gaussian Processes for Machine Learning — Carl E. Rasmussen, Christopher K. I. Williams (PDF; no signup). Rasmussen and Williams offer the definitive reference on Gaussian‑process models for regression and classification. MIT Press hosts a free PDF of the book as part of its open‑access program.
9. Information Theory, Inference, and Learning Algorithms — David J. C. MacKay (HTML/PDF; no signup). MacKay’s eclectic text blends information theory with inference and coding, culminating in applications such as neural networks and Bayesian inference. The author provides chapter‑by‑chapter HTML pages and downloadable PDFs from his website.
10. Understanding Machine Learning: From Theory to Algorithms — Shai Shalev‑Shwartz, Shai Ben‑David (PDF; no signup). This graduate‑level textbook gives a principled introduction to the algorithmic foundations of machine learning, covering VC dimension, boosting and kernel methods. The authors allow free download of the PDF for personal use.
11. Interpretable Machine Learning — Christoph Molnar (HTML/PDF; no signup). Molnar’s open‑source book surveys interpretability methods such as partial‑dependence plots, SHAP values and counterfactual explanations. Regularly updated through GitHub, it has become a key resource for practitioners seeking to make black‑box models more transparent.
12. Fairness and Machine Learning: Limitations and Opportunities — Solon Barocas, Moritz Hardt, Arvind Narayanan (HTML/PDF; no signup). This work analyses fairness, accountability and transparency in machine‑learning systems. The authors discuss bias, discrimination and possible interventions, and they provide a freely downloadable PDF alongside a living HTML version.
13. Computer Vision: Algorithms and Applications (2nd ed. draft) — Richard Szeliski (PDF; no signup). Covering image formation, feature detection, stereo vision and 3‑D reconstruction, this widely used text serves both as a reference and as course material. The author has made the second‑edition draft PDF available for free download.
14. Speech and Language Processing (3rd ed. online draft) — Daniel Jurafsky, James H. Martin (HTML; no signup). The draft of the third edition of this influential NLP textbook is hosted openly, with chapters on language models, transformers and dialog systems. Readers can follow along as the authors update the content to reflect the latest research.
15. Graph Representation Learning — William L. Hamilton (PDF; no signup). Hamilton’s concise book introduces techniques for learning on graphs, including node embeddings, graph neural networks and applications to knowledge graphs. A free PDF is provided on the author’s website.
16. A Course in Machine Learning — Hal Daumé III (PDF; no signup). Originally lecture notes, this book emphasizes understanding over formalism and covers decision trees, perceptrons, kernels and structured prediction. The author maintains a free PDF and invites feedback from learners.
17. Bayesian Reasoning and Machine Learning — David Barber (PDF; no signup). Barber’s text uses a probabilistic framework to cover graphical models, variational inference and sampling methods. The full PDF is freely accessible, accompanied by MATLAB code examples.
18. Neural Networks and Deep Learning — Michael A. Nielsen (HTML; no signup). Nielsen’s interactive online book introduces neural networks using clear prose, interactive visualizations and Python exercises. It focuses on intuitively explaining backpropagation, gradient descent and convolutional nets.
19. Introduction to Information Retrieval — Christopher D. Manning, Prabhakar Raghavan, Hinrich Schütze (HTML/PDF; no signup). This classic covers indexing, vector‑space models, web search and text classification. The authors host the full HTML edition and a PDF on their Stanford page.
20. Convex Optimization — Stephen Boyd, Lieven Vandenberghe (PDF; no signup). Boyd and Vandenberghe’s book is a staple for anyone studying optimization, providing theory and applications from signal processing to machine learning. A free PDF is provided on the authors’ website.
21. Bandit Algorithms — Tor Lattimore, Csaba Szepesvári (HTML/PDF; no signup). This open‑access text covers multi‑armed bandits, regret bounds and reinforcement learning connections. The authors maintain both HTML chapters and a printable PDF.
22. Algorithms for Reinforcement Learning — Csaba Szepesvári (PDF; no signup). This concise book focuses on fundamental RL algorithms such as Monte‑Carlo methods, temporal‑difference learning and policy gradients. It is available as a free PDF on the author’s site.
23. Reinforcement Learning and Optimal Control — Dimitri P. Bertsekas (PDF; no signup). Bertsekas offers a rigorous treatment of RL and dynamic programming, highlighting connections to optimal control. A full PDF is provided free for personal use.
24. Deep Learning for Coders with fastai and PyTorch — Jeremy Howard, Sylvain Gugger (HTML/Jupyter notebooks; no signup). Targeted at practitioners, this book teaches deep learning through hands‑on coding examples with fastai and PyTorch. The entire text and accompanying notebooks are freely accessible on the fast.ai website.
25. Machine Learning Yearning — Andrew Ng (PDF; no signup). Ng’s concise guide helps engineers and product managers understand how to structure machine‑learning projects. The PDF is distributed at no cost and covers topics like error analysis, data collection and model deployment.
26. Probabilistic Machine Learning: An Introduction — Kevin P. Murphy (HTML/PDF; no signup). The first volume of Murphy’s new series introduces probabilistic models and inference techniques with many modern examples. A draft PDF and HTML version are freely available under a Creative‑Commons license
27. Machine Learning: A Probabilistic Perspective — Kevin P. Murphy (PDF; no signup). Murphy’s earlier 2012 textbook remains a comprehensive reference, covering Bayesian networks, graphical models and variational inference. The author provides a free PDF version for personal use
28. Foundations of Data Science — Avrim Blum, John Hopcroft, Ravindran Kannan (PDF; no signup). This draft text blends algorithms, machine learning and statistics, highlighting randomized algorithms, spectral methods and clustering. Cornell University hosts the complete PDF freely
29. Foundations of Machine Learning — Mehryar Mohri, Afshin Rostamizadeh, Ameet Talwalkar (PDF/HTML; no signup). This MIT Press book formalizes learning theory concepts such as VC dimension, Rademacher complexity and kernel methods. The publisher makes the PDF and HTML versions freely available under a Creative‑Commons license
30. Algorithms for Decision Making — Mykel J. Kochenderfer, Tim A. Wheeler, Kyle H. Wray (PDF; no signup). The text applies decision‑making under uncertainty to robotics and autonomous systems, discussing Markov decision processes, POMDPs and planning algorithms. MIT Press hosts a complete PDF under a CC BY‑NC‑ND license
31. Reinforcement Learning: Theory and Algorithms — Alekh Agarwal, Nanjiang Yuan, Sham Kakade, Michael J. Kearns, Alexander Rakhlin, Ambuj Tewari (PDF; no signup). This working draft surveys modern RL theory, including regret analysis, policy‑gradient methods and exploration strategies. A free PDF is available through the authors’ repository
32. Automated Machine Learning: Methods, Systems, Challenges — Frank Hutter, Lars Kotthoff, Joaquin Vanschoren (eds.) (PDF/HTML; no signup). This open‑access book covers hyperparameter optimization, neural‑architecture search and AutoML systems. The preface states that it is distributed under a Creative‑Commons license and may be downloaded freely
33. Probabilistic Programming and Bayesian Methods for Hackers — Cameron Davidson‑Pilon (Jupyter notebooks/PDF; no signup). This open‑source book introduces Bayesian inference through interactive Python notebooks, using real‑world datasets and intuitive explanations. The GitHub repository notes that the book is under the MIT license and can be freely copied and modified
34. Think Bayes — Allen B. Downey (HTML/PDF; no signup). Downey’s text teaches Bayesian statistics using Python, focusing on coding rather than mathematical derivations. The author explains that readers are free to copy, distribute and modify the book as long as they attribute and share‑alike
35. The Hundred‑Page Machine Learning Book — Andriy Burkov (PDF chapters; no signup). Burkov distills key machine‑learning concepts into a slim volume that covers supervised, unsupervised and reinforcement learning. The book’s “read‑first, buy‑later” principle allows free downloading of chapters under a CC BY‑SA license
36. Machine Learning Engineering — Andriy Burkov (PDF; no signup). This companion to the Hundred‑Page book focuses on building reliable ML systems, covering design patterns, data pipelines and monitoring. The author describes a “read‑first, buy‑later” approach and releases a free PDF for personal use
37. The Little Book of Deep Learning — François Fleuret (PDF; no signup). Originally designed to be read on a phone, this concise booklet introduces deep‑learning basics and key models. The website notes that the book is licensed under a non‑commercial Creative‑Commons license and offers phone‑formatted and printable PDFs
38. Applied Causal Inference — Robert Osgood, others (HTML; no signup). Osgood’s web‑based book teaches causal inference using graphical models, propensity scores and difference‑in‑differences. The website explains that the web version is free of charge and invites readers to donate or purchase the paperback
39. Artificial Intelligence: Foundations of Computational Agents (2nd edition) — David L. Poole, Alan K. Mackworth (HTML; no signup). This undergraduate‑level AI textbook covers search, logic, planning and machine learning, with a new chapter on ethics. The authors provide the full HTML edition online and note that it is available under a Creative‑Commons license
40. A Brief Introduction to Machine Learning for Engineers — Osvaldo Simeone (PDF; no signup). This short text offers an engineer‑friendly overview of key ML concepts, contrasting discriminative vs. generative models and frequentist vs. Bayesian approaches. The freecomputerbooks page describes it as an open introduction to fundamental machine‑learning concepts
41. Unlocking Artificial Intelligence: From Theory to Applications — Yongjian Yu, Shoucheng Chen, Anwen Yu (PDF/EPUB; no signup). This 2024 open‑access book surveys AI and machine‑learning techniques and their applications in areas like natural‑language processing and recommendation systems. Springer’s page notes that the book is open access and provides a free PDF download

Last but not least, this outstanding book with 1151 pages will definitely get you up to speed in AI:

42. Artificial Intelligence – A Modern Approach — Stuart J. Russell and Peter Norvig

The post 42 Free AI Books (PDF/HTML) appeared first on Be on the Right Side of Change.

What Is ChatGPT Doing … and Why Does It Work? by Stephen Wolfram

Description: “Nobody expected this—not even its creators: ChatGPT has burst onto the scene as an AI capable of writing at a convincingly human level. But how does it really work? What’s going on inside its “AI mind”? In this short book, prominent scientist and computation pioneer Stephen Wolfram provides a readable and engaging explanation that draws on his decades-long unique experience at the frontiers of science and technology. Find out how the success of ChatGPT brings together the latest neural net technology with foundational questions about language and human thought posed by Aristotle more than two thousand years ago.”

Read the book for free here (HTML)

Large Language Models at Work by Vlad Riscutia

Description: “This book is aimed at software engineers wanting to learn about how they can integrate large language models into their software systems. It covers all the necessary domain concepts and comes with simple code samples. No prior AI knowledge required to understand this book, just basic programming. After reading the book, one should have a solid understanding of all the required pieces to build a large language model-powered solution and the various things to keep in mind (like non-determinism, AI safety & security, and so on).”

Read the book for free on GitHub (Web)

The Ultimate ChatGPT Guide

Description: “ChatGPT is the most powerful natural language AI ever created. More than 100 resources to help you learn how to use ChatGPT to enhance your life. This Guide includes: 60+ Chapters, 100+ AI Tools and Resources, Save 100+ hours on research”

This is actually a free Gumroad course (“Pay What You Want”)

Speech and Language Processing by Jurafsky and Martin

Blurb: This book on Natural Language Processing (NLP) is a thorough and updated guide, ideal for anyone interested in the field, from students to seasoned practitioners. It delves into the core algorithms and techniques that form the foundation of NLP, as well as exploring various applications and the intricacies of linguistic structure. The text is divided into three key sections.

The first part focuses on fundamental algorithms, introducing the reader to the basics such as text normalization and edit distance, and advances to complex topics like neural networks and language models. This section blends newly written content with updated material from the previous edition.

The second part shifts the focus to practical applications of NLP, covering a wide range of topics from machine translation and chatbots to speech recognition, demonstrating the real-world utility of the techniques discussed.

The final section is dedicated to the annotation of linguistic structures. It offers an in-depth exploration of grammatical and semantic aspects, including parsing, sentence meaning, and discourse coherence, providing a comprehensive understanding of language processing.

Read the PDF on Stanford.edu here (PDF) or the HTML and PowerPoint version here (HTML, pptx)

Foundations of Statistical Natural Language Processing by Manning/Schütze

Description: Statistical approaches to processing natural language text have become dominant in recent years. This foundational text is the first comprehensive introduction to statistical natural language processing (NLP) to appear. The book contains all the theory and algorithms needed for building NLP tools. It provides broad but rigorous coverage of mathematical and linguistic foundations, as well as detailed discussion of statistical methods, allowing students and researchers to construct their own implementations. The book covers collocation finding, word sense disambiguation, probabilistic parsing, information retrieval, and other applications.

Read the PDF here (+Download) and Stanford Online (HTML)

Pattern Recognition and Machine Learning by Christopher M. Bishop

Description: This is the first textbook on pattern recognition to present the Bayesian viewpoint. The book presents approximate inference algorithms that permit fast approximate answers in situations where exact answers are not feasible. It uses graphical models to describe probability distributions when no other books apply graphical models to machine learning. No previous knowledge of pattern recognition or machine learning concepts is assumed. Familiarity with multivariate calculus and basic linear algebra is required, and some experience in the use of probabilities would be helpful though not essential as the book includes a self-contained introduction to basic probability theory.

Read the PDF here (+Download)

Natural Language Processing with Python by Bird, Klein, Loper

Description: This book offers a highly accessible introduction to natural language processing, the field that supports a variety of language technologies, from predictive text and email filtering to automatic summarization and translation. With it, you’ll learn how to write Python programs that work with large collections of unstructured text. You’ll access richly annotated datasets using a comprehensive range of linguistic data structures, and you’ll understand the main algorithms for analyzing the content and structure of written communication.

Read the book here (HTML)

The Illustrated Transformer

Also check out this excellent resource on LLMs that is nicely illustrated:

The Illustrated Transformer

The post Top 7 Free LLM Books (100% Trustworthy Links) appeared first on Be on the Right Side of Change.

Why “Deep Learning” the book? It’s the definitive textbook in the field, “Deep Learning” covers a comprehensive range of topics, from the foundational concepts to the advanced techniques driving the latest innovations in artificial intelligence.

Why watching videos instead of reading the book? I found the world of deep learning to be a complex maze of mathematical equations and abstract concepts. However, watching Ian Goodfellow and other experts break down each chapter with real-world examples and intuitive explanations transformed my perspective. Watching videos is like having a personal tutor guiding me through the intricacies of neural networks, making the once-daunting subject both accessible and fascinating.

That’s why I have chosen one video for each book chapter, preferably from one of the authors, so you can “read the book” in a more interactive multimodal format.

Lesson 1 – Introduction

Read Chapter

Lesson 2 – Linear Algebra

Read Chapter

Lesson 3 – Probability and Information Theory

Read Chapter

Lesson 4 – Numerical Computation

Read Chapter

Lesson 5 – Machine Learning Basics

This is a playlist based on Chapter 5 of the Deep Learning book, if you need a quick refresher on ML, feel free to watch the whole playlist!

Read Chapter

Lesson 6 – Deep Feedforward Networks

Read Chapter

Lesson 7 – Regularization for Deep Learning

Read Chapter

Lesson 8 – Optimization for Training Deep Models

Read Chapter

Lesson 9 – Convolutional Networks

Read Chapter

Lesson 10 – Sequence Modeling: Recurrent and Recursive Networks

Read Chapter

Lesson 11 – Practical Methodology

Read Chapter

Lesson 12 – Applications

Read Chapter

Lesson 13 – Linear Factors

Read Chapter

Lesson 14 – Autoencoders

Read Chapter

Lesson 15 – Representation Learning

Read Chapter

Lesson 16 – Structured Probabilistic Models for Deep Learning

Read Chapter

Lesson 17 – Monte Carlo Methods

Read Chapter

Lesson 18 – Confronting the Partition Function

Read Chapter

Congratulations, you’ve just reached the top elite coders proficient in theory and practice of the most essential invention in computer science — deep neural networks!

Recommended: Gradient Descent in Neural Nets – A Simple Guide to ANN Learning

Feel free to check out the following Finxter Academy course with a downloadable PDF course certificate for your CV or bio:

The post Diving Deep into ‘Deep Learning’ – An 18-Video Guide by Ian Goodfellow and Experts appeared first on Be on the Right Side of Change.

• CNNs have long been a staple in image recognition and computer vision tasks, thanks to their ability to efficiently learn local patterns and spatial hierarchies in images. They employ convolutional layers and pooling to reduce the dimensionality of input data while preserving critical information. This makes them highly suitable for tasks that demand interpretation of visual data and feature extraction.
• Transformers, originally developed for natural language processing tasks, have gained momentum due to their exceptional performance and scalability. With self-attention mechanisms and parallel processing capabilities, they can effectively handle long-range dependencies and contextual information. While their use in computer vision is still limited, recent research has begun to explore their potential to rival and even surpass CNNs in certain image recognition tasks.

CNNs and Transformers differ in their architecture, focus domains, and coding strategies. CNNs excel in computer vision, while Transformers show exceptional performance in NLP; although, with the development of ViTs, Transformers also show promise in the realm of computer vision.

CNN

Convolutional Neural Networks (CNNs) are designed primarily for computer vision tasks, where they excel due to their ability to apply convolving filters to local features. This architecture has also proven effective for NLP, as evidenced by their success in semantic parsing and search query retrieval.

A CNN can efficiently handle large amounts of input data which makes them suitable for computer vision tasks as mentioned before.

CNNs are composed of multiple convolutional layers that apply filters to the input data.

These filters, also known as kernels, are responsible for detecting patterns and features within an image. As you progress through the layers, the filters can identify increasingly complex patterns and ultimately help classify the image.

One of the key advantages of using CNNs is their efficient computation, which significantly reduces the number of parameters required for training.

Transformers

Transformers, on the other hand, have become the go-to architecture in NLP tasks such as text classification, sentiment analysis, and machine translation. The key to their success lies in the attention mechanism, which enables them to efficiently handle long-range dependencies and varied input lengths. Vision Transformers (ViTs) are now also being employed in computer vision tasks, opening up new possibilities in this field.

Transformers have gained a lot of attention in recent years due to their extraordinary capabilities across various domains such as natural language processing and computer vision. In this section, you’ll learn more about the key components and advantages of transformers.

For those interested in coding these models from scratch, CNNs utilize layers with convolving filters and activation functions, while Transformers involve multi-head self-attention, positional encoding, and feed-forward layers. The code for these architectures can vary depending on the particular use-case and the design of the model.

To start with, transformers consist of an encoder and a decoder.

The encoder processes the input sequence, while the decoder generates the output sequence. Central to the functioning of transformers is their ability to handle position information smartly. This is achieved through the use of positional encodings, which are added to the input sequence to retain information about the position of each element in the sequence.

“Each decoder block receives the features from the encoder. If we draw the encoder and the decoder vertically, the whole picture looks like the diagram from the paper.” (Source)

One of the fundamental aspects of transformers is the self-attention mechanism. This allows the model to weigh the importance of each element in the input sequence in relation to other elements, providing a more nuanced understanding of the input. It is this mechanism that contributes to the excellent performance of transformers for tasks involving multiple modalities, such as text and images, where context is crucial.

A key advantage of transformers is their ability to process input sequences in parallel, enabling parallelization and making them more computationally efficient compared to recurrent neural networks (RNNs) or convolutional neural networks (CNNs). This efficiency is partly due to their architecture, which employs layers of Multi-Head Attention and Multi-Layer Perceptrons (MLPs). These components play a significant role in extracting diverse patterns from the data and can be scaled as needed.

It is worth noting that transformers typically have a large number of parameters, which contributes to their high performance capabilities across various tasks. However, this can also result in increased complexity and longer inference times, as well as an increased need for computational resources. While these factors may be a concern in certain situations, the overall benefits of transformers continue to drive their popularity and adoption in numerous applications such as ChatGPT.

Recommended: Alien Technology: Catching Up on LLMs, Prompting, ChatGPT Plugins & Embeddings

Comparison of CNN and Transformer

One key distinction is that CNNs leverage inductive biases that encode spatial information from neighboring pixels, whereas Transformers use self-attention mechanisms to process the input.

Beginning with the competitive performance of these models, CNNs have long been the go-to solution for image recognition tasks. Many popular architectures, such as ResNet, have demonstrated exceptional performance on a variety of tasks.

However, recent advancements in Vision Transformers (ViT) have shown that transformers are now on par with or even surpassing the accuracy of CNN-based models in certain instances.

Regarding accuracy, due to advancements in self-attention mechanisms, Transformers tend to perform well on tasks involving longer-range dependencies and complex contextual information. This is especially useful in natural language processing (NLP) tasks. CNNs primarily excel in tasks focusing on local spatial patterns, such as image recognition, where input data exhibits strong spatial correlations.

Inductive biases play a crucial role in the performance of CNNs. They enforce the idea of locality in image data, ensuring that nearby pixels tend to be more strongly connected. These biases help CNNs learn and extract useful features from images, such as edges, corners, and textures, which contribute to their effectiveness in computer vision tasks. Transformers, on the other hand, do not rely heavily on such biases and instead use the self-attention mechanism to capture relationships between elements in the input data.

The way both architectures handle neighboring pixel information differs as well. CNNs use convolutional layers to detect local patterns and maintain spatial information throughout the network. Transformers, however, first convert input images into a sequence of tokens, effectively losing the spatial connections between the pixels. The self-attention mechanism is then used to model relationships between these tokens.

While CNNs have a long history of success in image recognition tasks, there has been a steady increase in the adoption of Transformers for various computer vision tasks.

Applications in Language Processing

In the field of natural language processing (NLP), both Transformer models and Convolutional Neural Networks (CNNs) have made significant contributions.

One common NLP task is machine translation, which involves converting text from one language to another. Transformers have become quite popular in this domain, as they can effectively capture long-range dependencies, a crucial aspect of translating complex sentences. With their self-attention mechanism, they have the ability to pay attention to every word in the input sequence, leading to high-quality translations.

For language modeling tasks, where the goal is to predict the next word in a given sequence, Transformers have also shown remarkable performance.

By capturing long-range dependencies and leveraging large amounts of context information, Transformer models are well-suited for language modeling problems. This has led to the development of powerful pre-trained language models like BERT and GPT-3 and GPT-4, which have set new benchmarks in various NLP tasks.

On the other hand, CNNs have proven their effectiveness in tasks that involve a fixed-size input, such as sentence classification. With their ability to capture local patterns through convolutional layers, CNNs can learn meaningful textual representations. However, for tasks that require capturing dependencies across larger contexts, they may not be as suitable as Transformer models.

While working with Transformer models, it is essential to keep in mind that they require more memory and computational resources than CNNs, mainly due to their self-attention mechanism. This could be a limitation if you are working with resource constraints.

Recommended: Claude 2 LLM Reads Ten Papers in One Prompt with Massive 200k Token Context

Applications in Computer Vision

One common computer vision task where these models excel is image classification. With CNNs, you can effectively learn to identify features in images by applying a series of filters through convolutional layers. These networks create simplified versions of the input image by generating feature maps, highlighting the most relevant parts of the image for classification purposes.

On the other hand, transformers, such as the Vision Transformer (ViT), have been recently proposed as alternatives to classical convolutional approaches. They relax the translation-invariance constraint of CNNs by using attention mechanisms, allowing them to learn more flexible representations of the input images, potentially leading to better classification performance.

Another critical application in computer vision is object detection. Both deep learning techniques, CNNs and vision transformers, have been instrumental in driving significant advances in this area.

Object detection models based on CNNs have paved the way for more accurate and efficient detection systems, while transformers are being explored for their potential to model long dependencies between input elements and parallel processing capabilities, which could lead to further improvements.

In addition to these popular tasks, CNNs and transformers have also been applied to other computer vision challenges such as semantic segmentation, where each pixel in an image is assigned a class label, and instance segmentation, which requires classifying and localizing individual instances of objects.

These applications require models that can effectively learn spatial hierarchies and representations, which both CNNs and transformers have demonstrated their capability to do.

Recommended: I Created My First DALL·E Image in Python OpenAI Using Four Easy Steps

Frequently Asked Questions

What makes Transformers more effective than CNNs?

Transformers are designed to handle long-range dependencies in sequences effectively due to the self-attention mechanism. This allows them to process and encode information from distant positions in the data efficiently. On the other hand, CNNs use local convolutions, which may not capture large-scale patterns as efficiently. Transformers also parallelize sequence processing, leading to faster computations.

How do Transformers and CNNs perform in computer vision tasks?

CNNs have been the dominant approach in computer vision tasks, such as image classification and object detection, due to their effectiveness in learning local features and hierarchical representations. Transformers, though successful in NLP, have recently started to gain traction in computer vision tasks. Some research suggests that Transformers can perform well and even outpace CNNs in certain computer vision tasks, especially when handling large images with complex patterns.

Can Transformers replace CNNs for image processing?

Transformers are a promising alternative to CNNs for image processing tasks, but they may not replace them entirely. CNNs remain effective and efficient for many computer vision problems, especially when dealing with smaller images or limited computational resources. However, as the field advances, it’s possible that we will see more applications where Transformers outperform or complement CNNs.

What are the advantages of CNN-Transformer hybrids?

CNN-Transformer hybrids combine the strengths of both architectures. CNNs excel at capturing local features, while Transformers efficiently handle dependencies across larger distances. By using a hybrid, you can leverage the benefits of both, leading to improved performance in various tasks, from image classification to semantic segmentation.

How does Transformer architecture compare to RNN and CNN?

All three models have unique strengths. RNNs are known for their ability to handle sequential data and model temporal dependencies but may suffer from the vanishing gradient problem in long sequences. CNNs excel at processing spatial data and learning hierarchical representations, making them effective for many image processing tasks. Transformers emerged as a powerful alternative for handling long sequences and parallelizing computations, which led to their success in NLP and, more recently, computer vision.

Why is Transformer inference speed important compared to CNN?

Inference speed is critical in many real-world applications, such as autonomous driving or real-time video analysis, where quick decisions are crucial. With their parallel computation capabilities, Transformers offer potential speed advantages over CNNs, especially when dealing with large sequences or images. Faster inference times could provide a competitive edge for various applications and contribute to the growing interest in Transformers in the computer vision domain.

Recommended: Best 35 Helpful ChatGPT Prompts for Coders (2023)

Prompt Engineering with Python and OpenAI

You can check out the whole course on OpenAI Prompt Engineering using Python on the Finxter academy. We cover topics such as:

• Embeddings
• Semantic search
• Web scraping
• Query embeddings
• Movie recommendation
• Sentiment analysis

Academy: Prompt Engineering with Python and OpenAI

The post Transformers vs Convolutional Neural Nets (CNNs) appeared first on Be on the Right Side of Change.

ChatGPT Is Only The Beginning

In recent years, AI research has focused on increasing compute power, which has led to impressive improvements in model performance. In 2020, OpenAI demonstrated that bigger models with more parameters could yield better returns than simply adding more data with their paper on Scaling Laws for Neural Language Models.

This research paper explores how the performance of language models changes as we increase the model’s size, the amount of data used to train it, and the computing power used in training.

The authors found that the performance of these models, measured by their ability to predict the next word in a sentence, improves in a predictable way as we increase these factors, with some trends continuing over a wide range of values.

For example, a model that’s 10 times larger or trained on 10 times more data will perform better, but the exact improvement can be predicted by a simple formula.

Interestingly, other factors like how many layers the model has or how wide each layer is don’t have a big impact within a certain range. The paper also provides guidelines for training these models efficiently.

For instance, it’s often better to train a very large model on a moderate amount of data and stop before it fully adapts to the data, rather than using a smaller model or more data.

In fact, I’d argue that transformers, the technology behind large language models are the real deal as they just don’t converge:

This development sparked a race among companies to create models with more and more parameters, such as GPT-3 with its astonishing 175 billion parameters. Microsoft even released DeepSpeed, a tool designed to handle (in theory) trillions of parameters!

Recommended: Transformer vs LSTM: A Helpful Illustrated Guide

Model Size! (… and Training Data)

However, findings from DeepMind’s 2022 paper Training Compute – Optimal Large Language Models indicate that it’s not just about model size – the number of training tokens (data) also plays a crucial role. Until recently, many large models were trained using about 300 billion tokens, mainly because that’s what GPT-3 used.

DeepMind decided to experiment with a more balanced approach and created Chinchilla, a Large Language Model (LLM) with fewer parameters—only 70 billion—but a much larger dataset of 1.4 trillion training tokens. Surprisingly, Chinchilla outperformed other models trained on only 300 billion tokens, regardless of their parameter count (whether 300 billion, 500 billion, or 1 trillion).

What Does This Mean for You?

First, it means that AI models are likely to significantly improve as we throw more data and more compute on them. We are nowhere near the upper ceiling of AI performance by simply scaling up the training process without needing to invent anything new.

This is a simple and straightforward exercise and it will happen quickly and help scale these models to incredible performance levels.

Soon we’ll see significant improvements of the already impressive AI models.

How the AI Scaling Laws May Be as Important as Moore’s Law

Accelerating Technological Advancements: Just as Moore’s Law predicted a rapid increase in the power and efficiency of computer chips, the scaling laws in AI could lead to a similar acceleration in the development of AI technologies. As AI models become larger and more powerful, they could enable breakthroughs in fields such as natural language processing, computer vision, and robotics. This could lead to the creation of more advanced and capable AI systems, which could in turn drive further technological advancements.

Economic Growth and Disruption: Moore’s Law has been a key driver of economic growth and innovation in the tech industry. Similarly, the scaling laws in AI could lead to significant economic growth and disruption across various industries. As AI technologies become more powerful and efficient, they could be used to automate tasks, optimize processes, and create new business models. This could lead to increased productivity, reduced costs, and the creation of new markets and industries.

Societal Impact: Moore’s Law has had a profound impact on society, enabling the development of technologies such as smartphones, the internet, and social media. The scaling laws in AI could have a similar societal impact, as AI technologies become more integrated into our daily lives. AI systems could be used to improve healthcare, education, transportation, and other areas of society. This could lead to improved quality of life, increased access to resources, and new opportunities for individuals and communities.

Frequently Asked Questions

How can neural language models benefit from scaling laws?

Scaling laws can help predict the performance of neural language models based on their size, training data, and computational resources. By understanding these relationships, you can optimize model training and improve overall efficiency.

What’s the connection between DeepMind’s work and scaling laws?

DeepMind has conducted extensive research on scaling laws, particularly in the context of artificial intelligence and deep learning. Their findings have contributed to a better understanding of how model performance scales with various factors, such as size and computational resources. OpenAI has then pushed the boundary and scaled aggressively to reach significant performance improvements with GPT-3.5 and GPT-4.

How do autoregressive generative models follow scaling laws?

Autoregressive generative models, like other neural networks, can exhibit scaling laws in their performance. For example, as these models grow in size or are trained on more data, their ability to generate high-quality output may improve in a predictable way based on scaling laws.

Can you explain the mathematical representation of scaling laws in deep learning?

A scaling law in deep learning typically takes the form of a power-law relationship, where one variable (e.g., model performance) is proportional to another variable (e.g., model size) raised to a certain power. This can be represented as: Y = K * X^a, where Y is the dependent variable, K is a constant, X is the independent variable, and a is the scaling exponent.

Which publication first discussed neural scaling laws in detail?

The concept of neural scaling laws was first introduced and explored in depth by researchers at OpenAI in a paper titled “Language Models are Few-Shot Learners”. This publication has been instrumental in guiding further research on scaling laws in AI.

Here’s a short excerpt from the paper:

OpenAI Paper:

“Here we show that scaling up language models greatly improves task-agnostic, few-shot performance, sometimes even reaching competitiveness with prior state-of-the-art fine-tuning approaches.

Specifically, we train GPT-3, an autoregressive language model with 175 billion parameters, 10x more than any previous non-sparse language model, and test its performance in the few-shot setting.

[…]

GPT-3 achieves strong performance on many NLP datasets, including translation, question-answering, and cloze tasks, as well as several tasks that require on-the-fly reasoning or domain adaptation, such as unscrambling words, using a novel word in a sentence, or performing 3-digit arithmetic.”

Is there an example of a neural scaling law that doesn’t hold true?

While scaling laws can often provide valuable insights into AI model performance, they are not always universally applicable. For instance, if a model’s architecture or training methodology differs substantially from others in its class, the scaling relationship may break down, and predictions based on scaling laws might not hold true.

Recommended: 6 New AI Projects Based on LLMs and OpenAI

The post AI Scaling Laws – A Short Primer appeared first on Be on the Right Side of Change.

These open-source models have gained significant traction due to their impressive language generation capabilities. In this article, we will delve into the intricacies of each model to help you better understand their applications and differences.

GPT4All, an ecosystem for free and offline open-source chatbots, utilizes LLaMA and GPT-J backbones to train its model. Alpaca, on the other hand, offers an API/SDK for language tasks and is known for its availability and ease of use.

I created a table showcasing the similarities and differences of GPT4all, Llama, and Alpaca:

GPT4All Overview

GPT4All is an open-source project that aims to bring the capabilities of GPT-4, a powerful language model, to a broader audience. By developing a simplified and accessible system, it allows users like you to harness GPT-4’s potential without the need for complex, proprietary solutions.

Recommended: GPT4All Quickstart – Offline Chatbot on Your Computer

The GPT4All project team was inspired by ALPACA, another prominent language model. They used this inspiration to curate a dataset consisting of approximately 800k prompt-response samples, from which they generated 430k high-quality assistant-style training pairs. These pairs encompass a diverse range of content such as code, dialogue, and stories, broadening GPT4All’s potential applications for you.

Available on GitHub, GPT4All is designed for developers like yourself who are eager to leverage GPT-4’s capabilities without having to start from scratch. It provides you with a straightforward starting point for implementing GPT-4-based solutions in various scenarios and industries.

One popular use case for GPT4All is local chatbots. It offers you a free alternative to cloud-based services, allowing you to conveniently deploy your personalized language model directly on your machine without incurring ongoing costs or relying on remote servers.

TLDR: GPT4All is a versatile and accessible alternative to proprietary language model implementations such as GPT-4. Its open-source nature and wide range of relevant content make it an appealing choice for developers like you looking to harness the power of advanced language models.

Alpaca Overview

Alpaca is a popular large language model (LLM) from Stanford researchers that has gained significant attention in the AI community. With its impressive capabilities, many developers and businesses prefer using Alpaca for various natural language processing tasks.

When you start working with Alpaca, you’ll come across the source code on its GitHub repository. Here, you can observe numerous stars and rating indicators, reflecting the model’s credibility and wide acceptance among users.

Recommended: 11 Best ChatGPT Alternatives

To better suit your specific use case, you can finetune Alpaca through pre-built applications or create a custom app tailored to your needs. This flexibility makes the model appealing to developers aiming to address unique challenges.

Short Summary: Instruction-following models like GPT-3.5 and ChatGPT are powerful but have flaws. Academia faces challenges in researching these models due to limited access.

Stanford researchers fine-tuned Alpaca, a language model based on Meta’s LLaMA 7B, using 52K instruction-following demonstrations from text-davinci-003.

Alpaca is small, easy to reproduce, and shows similar behaviors to text-davinci-003. The team is releasing their training recipe and data, with plans to release model weights and an interactive demo for academic research.

Commercial use is prohibited due to licensing restrictions and safety concerns.

Image credit

Alpaca is a remarkable chatbot alternative to ChatGPT that you can explore. Developed by Stanford researchers, it was fine-tuned using Facebook’s LLaMA to deliver impressive language capabilities source.

One important aspect of Alpaca is its training process. It has been trained on massive datasets, enabling the model to understand and generate human-like text. This thorough training ensures that your app delivers accurate and coherent results.

In addition to its core capabilities, Alpaca offers an associated app called Alpaca-Lora. This app further enhances the features and usability of the Alpaca model, allowing for seamless integration with your projects.

Key Features Comparison

Text Generation Capabilities

When comparing Alpaca and GPT4All, it’s important to evaluate their text generation capabilities. Alpaca, an instruction-finetuned LLM, is introduced by Stanford researchers and has GPT-3.5-like performance. On the other hand, GPT4All features GPT4All-J, which is compared with other models like Alpaca and Vicuña in ChatGPT applications. Both models can effectively engage in tasks like ticketing, handling emails, or chatting with users.

Training Data and Models

The training data and versions of LLMs play a crucial role in their performance. Alpaca is based on the LLaMA framework, while GPT4All is built upon models like GPT-J and the 13B version. Models like Vicuña, Dolly 2.0, and others are also part of the open-source ChatGPT ecosystem.

Platform Support and Documentation

For platform support, you should explore the available APIs, libraries, and interfaces of Alpaca and GPT4All. Tools such as Alpaca.cpp, LLaMA.cpp, and Text-Generation-WebUI can help you experiment with these models on different platforms, including ONDE and Android. In addition, HuggingFace and repositories like Generative AI offer resources for integrating Alpaca and GPT4All into your projects.

Proper documentation is essential to ensure clear usage and understanding of these LLMs. Both Alpaca and GPT4All provide extensive resources for getting started, such as guides on optimization, training, and fine-tuning. For more information, you can visit their official websites or refer to popular forums like Reddit for additional insights and community support.

Use Cases

Business Applications

With GPT4All and Alpaca, you can leverage the power of LLMs for various business applications. Both LLaMA and GPT-4 models can be utilized to analyze and generate content for tasks like ticketing, composing emails, and creating documentation. By automating these tasks, you can improve efficiency and productivity within your organization.

Creative Writing

As a writer, you can use GPT4All and Alpaca for creative writing purposes. By leveraging the potential of these AI models, you can generate ideas for stories, characters, and plot developments. They can help you with brainstorming ideas for novels, screenplays, or short stories. All you need to do is provide a basic idea or starting point, and the AI models can assist you in expanding it into a well-rounded narrative.

Customer Support

Both GPT4All and Alpaca can be used to enhance customer support experiences. You can implement these models in your customer support systems to create AI-assisted agents that can handle support queries via chats or emails. With the help of these AI models, your support agents will have access to reliable information and resources to handle a variety of issues, from simple troubleshooting to complex problem-solving processes.

In addition to streamlining customer support services, incorporating AI like GPT4All and Alpaca can also help with creating accurate and user-friendly documentation, such as FAQs and knowledge base articles. This can aid customers in finding information they need without directly reaching out to your support team.

Licensing and Commercial Use

When choosing between GPT4All and Alpaca for your AI needs, it is essential to consider the licensing and commercial use aspects. You’ll find that both models offer different usage terms that might impact your projects and business developments.

GPT4All, powered by Nomic, is an open-source model based on LLaMA and GPT-J backbones. It has gained popularity in the AI landscape due to its user-friendliness and capability to be fine-tuned. Remarkably, GPT4All offers an open commercial license, which means that you can use it in commercial projects without incurring any subscription fees. This characteristic enables businesses and individuals alike to access and deploy GPT4All without having to worry about financial constraints.

On the other hand, Alpaca is another popular model developed by Stanford. It is known for its efficiency and powerful natural language processing features. However, it is essential to note that Alpaca’s licensing terms might differ from GPT4All’s, particularly concerning commercial use. Details about Alpaca’s commercial license are available on their website, and it is recommended that you thoroughly review them before making a decision.

Fine-Tuning

One crucial aspect that you should also consider is the ease of fine-tuning both models. You’ll want to choose a model that best suits your project’s requirements and which can be customized to meet your specific needs. In general, both GPT4All and Alpaca offer options for fine-tuning, but the availability of resources, tools, and documentation for each model should be evaluated, keeping in view your technical proficiency and the time you can invest in the development process.

Community and Support

When deciding between GPT4All and Alpaca, it’s essential to consider the community and support available for each project. You can find valuable resources and interact with other developers on their respective GitHub repositories.

GPT4All has an active and growing presence on GitHub, where you can find the source code, report issues, and contribute to the project. As of now, GPT4All has received significant attention in the open-source community, earning a considerable amount of GitHub stars, reflecting its popularity and reliability.

On the other hand, the Alpaca project also has a strong presence on GitHub. It has garnered a significant number of stars as well, showcasing its quality and the community’s interest. Additionally, you can find Android support for Alpaca, making it a suitable choice for integrating large language models into your mobile applications.

Beyond GitHub, both GPT4All and Alpaca are featured on the popular machine learning platform, Hugging Face. You can read model cards, explore the capabilities of each LLM, and find pretrained models for various tasks and languages. The Hugging Face community offers excellent support and a wide range of resources, keeping you well-equipped for your projects.

Conclusion

In comparing GPT4All and Alpaca, both are open-source language models that offer unique features and capabilities.

GPT4All is the result of a project team that curated approximately 800k prompt-response samples, refining them to 430k high-quality assistant-style prompt/generation training pairs. This model handles diverse content, including code, dialogue, and stories. If versatility and prompt-generating capabilities are important to you, GPT4All is worth considering.

On the other hand, Alpaca is inspired by GPT4All and focuses on providing a more assistive approach with its language model. It is designed for a wide range of applications, making it a strong contender in the world of large language models.

Frequently Asked Questions

What are the key differences between gpt4all and alpaca models?

GPT4All is a large language model (LLM) chatbot developed by Nomic AI, fine-tuned from the LLaMA 7B model, a leaked large language model from Meta (formerly known as Facebook). On the other hand, Alpaca is another LLM with its own set of features and capabilities. While both models aim to provide advanced and powerful LLM performance, they may have different training data, architecture, and fine-tuning processes.

How do gpt4all and alpaca compare in terms of performance?

To compare the performance of GPT4All and Alpaca, you may need to take into account factors such as response latency, accuracy, and the overall quality of generated content. As specific performance benchmarks and comparisons may vary, you should explore both models in the context of your specific use case and requirements to make an informed decision.

Are both gpt4all and alpaca open source models?

Yes, both GPT4All and Alpaca are open-source models. GPT4All’s source code and resources can be found on their GitHub repository, while Alpaca’s source code and resources are also available through their respective platform. This means that you can access, use, and customize these models as per your requirements.

What are the primary use cases for gpt4all and alpaca?

Both GPT4All and Alpaca models are designed for a wide range of Natural Language Processing (NLP) tasks. Some typical use cases may include generating human-like text, creative writing, content creation, automation of customer support, and conversational AI applications. Depending on your specific needs, you can choose the model that caters to your requirements and goals.

How do gpt4all and alpaca models handle multilingual tasks?

Multilingual support may vary between GPT4All and Alpaca models, depending on the training data and fine-tuning processes employed by each model. You should review the documentation and capabilities of each model to understand their support for different languages and how well they can handle multilingual tasks.

What are the system requirements and setup process for gpt4all and alpaca?

The system requirements and setup process for GPT4All and Alpaca models may vary. GPT4All models are designed to run locally on your own CPU, which may have specific hardware and software requirements. For Alpaca, it’s essential to review their documentation and guidelines to understand the necessary setup steps and hardware requirements. Keep in mind that large prompts and complex tasks can require longer computation time and more resources, affecting the overall performance of both models.

Recommended: I Tried Berkeley’s Gorilla Large Language Model

The post GPT4all vs Alpaca: Comparing Open-Source LLMs appeared first on Be on the Right Side of Change.

This model’s maximum context length is <8192> tokens. However, your messages resulted in <a Gazillion> tokens. Please reduce the length of the messages.

So was I. Now I have just discovered that the new "gpt-4-32k" model slowly rolls out, as reported by several early adopters.

Here’s an example API call you can issue if you already have access in the playground:

payload = {
    "model": "gpt-4-32k",
    "messages": [
        {"role": "system", "content": "You are William Shakespeare."},
        {"role": "user", "content": "Write a story on love."}
    ]
}

However, please note that only a selected group of people already has access to it. You can check it in the Playground > Mode > Chat > Model > gpt-4-32k.

This is how it looks if you don’t have access yet:

Some Interesting Facts on GPT-4-32k

OpenAI’s GPT-4 offers a larger context window of 32k tokens, providing a broad range of applications, including simplifying Q&A Chatbot creation by fitting entire databases into the 32k prompt and summarizing large data sets effectively. It can even interpret complex documents like the IRS tax code. However, the rollout of GPT-4 is based on a waitlist, with earlier joiners having quicker access.

• OpenAI released GPT-4 32k model to early adopters.
• It seems to be released in the order of joining the waitlist, probabilistically.
• The 32k model can handle 32,000 tokens of context.
• One token generally corresponds to ~4 characters of text for common English text, which equates to roughly ¾ of a word.
• The 32k model can thus process context equivalent to approximately 24,000 words.
• Regarding page count, this roughly translates to around 48-50 single-spaced pages of text.
• There’s a new tokenizer for GPT-4: https://tiktokenizer.vercel.app/.
• The cost of using this model is high, making it potentially inaccessible for wider usage; $0.60 for 20k prompt tokens.
• Some users are exploring chat history compression techniques to mitigate high usage costs.
• For code, depending on the language and formatting, the model can handle approximately 4.5k to 2k lines of code.
• There are ongoing discussions about possible strategies for managing larger document interactions within the 32k token limit.
• However, it’s worth noting that the OpenAI API is stateless, meaning the entire conversation, including its response, is limited to 32k tokens.

Personally, I’d use the 32,000 tokens as context, possibly integrating larger embeddings to provide a truly helpful, context-sensitive, intelligent Q&A bot. Read more here:

Recommended: Building a Q&A Bot with OpenAI: A Step-by-Step Guide to Scraping Websites and Answer Questions

How Many Words Can GPT-4-32k Generate (Max)?

32k tokens yield roughly 3/4 of 32k, i.e., 24k words.

How Many Pages Does GPT-4-32k Generate (Max)?

Assuming that each page has 500 words, that’s 24000/500 = 48 pages.

What If You Don’t Have Access to GPT-4-32k Yet?

As you wait for GPT-4-32k access, you can play with open-source models such as MosaicML which recently released a 64k tokens variant.

See here for more:

Recommended: MPT-7B: A Free Open-Source Large Language Model (LLM)

The post Looks Like GPT-4-32k is Rolling Out appeared first on Be on the Right Side of Change.

Recommended: 6 New AI Projects Based on LLMs and OpenAI

This surge in interest can be attributed to the incredible potential LLMs hold in tasks like text summarization, translation, and even content generation. As with any rapidly evolving field, keeping up with the latest open-source research is crucial for both newcomers and seasoned experts alike.

One remarkable example in this domain is OpenICL, an open-source framework designed specifically for in-context learning. This toolkit aims to streamline ICL research with its flexible architecture, enabling users to easily adapt it to suit their research and use cases.

Another notable instance is GPT-Neox-20B, a large-scale autoregressive language model that focuses on enhancing AI safety, interpretability, and understanding of LLM performance with different training data sets.

In addition to the above, the growing concern regarding LLMs’ ability to generate misleading or false information has prompted research into their detection mechanisms.

One such study is The Science of Detecting LLM-Generated Texts, which delves into the challenges associated with identifying machine-generated content—could be useful in some areas.

Overview of LLM Research

Large Language Models

Large Language Models (LLMs) have demonstrated impressive capabilities in various natural language processing tasks, including text generation, translation, and summarization. These models, such as OpenAI’s GPT-3 and Google’s BERT, consist of a massive number of parameters (billions) and are trained on large datasets comprising terabytes of text data.

LLMs learn to generate contextually meaningful text by ingesting vast amounts of textual data, known as tokens, during training. They efficiently deal with large sequences of tokens by incorporating attention mechanisms, transformer architectures, and other advanced machine-learning techniques.

Performance benchmarks are essential for evaluating LLMs, and popular ones include GLUE and SuperGLUE – benchmarking platforms for Natural Language Understanding (NLU). These benchmarks consist of various tasks that assess the models’ language understanding abilities.

Open-Source Platforms

Open-source platforms have become increasingly important in the LLM research landscape.

Hugging Face is a prominent player in this domain, providing an extensive library of pre-trained models, datasets, and tools for designing, training, and deploying LLMs. Its Transformers library supports multiple languages and is widely adopted for NLP research and applications.

Another open-source initiative, EleutherAI, focuses on advancing AI research through the collaborative development and distribution of open LLM resources. EleutherAI’s projects include the LLaMA dataset and the open-source LLM known as GPT-Neo. They aim to promote transparent research and facilitate the progress of LLM study by providing access to cutting-edge technology and research material.

Open-source ICL (in-context learning) platforms, such as the OpenICL toolkit, offer an accessible way for researchers to experiment with and assess LLMs’ performance. These platforms provide a user-friendly interface and a flexible architecture that allows users to easily develop, test, and evaluate LLMs’ capabilities using custom datasets and tasks.

The growth of LLM research has been accelerated by the availability of open-source platforms and resources. These tools facilitate collaboration and promote innovation, contributing significantly to the advancements we see today in language models, AI, and machine learning.

5 Promising LLMs in Research

ChatGPT

ChatGPT is a promising large language model that has made significant strides in the research community. It is an open-source LLM, offering numerous benefits to researchers and developers alike. ChatGPT is often fine-tuned on specific tasks, making it a versatile tool in various applications.

The fine-tuning process allows the model to perform well on different tasks, such as text generation or answer extraction. The community-driven approach behind ChatGPT fosters collaboration and promotes a collective effort in advancing LLM research.

Recommended: Best 35 Helpful ChatGPT Prompts for Coders (2023)

Alpaca

Alpaca is another groundbreaking open-source LLM that is gaining momentum in the research sphere. Like ChatGPT, Alpaca can be fine-tuned for specific tasks, enhancing its performance and adaptability.

Its open-source nature enables researchers to contribute to its development, providing a foundation for a diverse community to engage, share ideas, and collaborate in the LLM landscape.

Vicuna

Vicuna stands out as a transformative open-source LLM with a focus on optimizing the potential of LLMs in various applications. The Vicuna model can also be fine-tuned on domain-specific tasks, ensuring it remains relevant and useful across different fields of study.

The support for open-source LLMs like Vicuna encourages more researchers to engage in LLM research, fostering a rich and cooperative community dedicated to advancing our understanding of large language models.

Dolly from Databricks

Dolly is a cutting-edge open-source LLM developed by Databricks. The team behind Dolly has focused on creating an LLM that can efficiently process and generate human-like text responses. Using advanced training techniques, Dolly has shown promising results in multiple AI research fields.

Some key features of Dolly include:

• High-quality text generation
• Improved comprehension capabilities
• Efficient training process

To support the AI research community, Databricks has made Dolly available as an open-source project, allowing researchers and developers to leverage this powerful LLM for various applications.

Bloom

Bloom is an open-source LLM with 176B parameters designed to drive progress in the field of LLM research. With its open-source nature, Bloom allows research teams to review the underlying architecture and contribute to the ongoing development of the LLM.

Some notable aspects of Bloom encompass:

• Comprehensive evaluation metrics
• Open-source architecture for transparency
• Collaboration-driven development

The post A Quick and Dirty Dip Into Cutting-Edge Open-Source LLM Research appeared first on Be on the Right Side of Change.

When utilizing the ChatGPT API, one critical aspect is temperature, a hyperparameter that impacts the generated text’s creativity and randomness.

Temperature values can range from 0 to 1, with higher values such as 0.8 generating more diverse and unpredictable outputs, while lower values like 0.2 produce more focused and deterministic responses. This parameter offers flexibility in fine-tuning the outputs of ChatGPT to meet the requirements of different use cases or applications.

As you work with the ChatGPT API, it is essential to experiment and find the optimal temperature setting for your specific needs. Striking a balance between randomness and determinism can help deliver the desired creativity and coherence in your AI-generated text.

ChatGPT API Overview

GPT-3, GPT-3.5-Turbo, and GPT-4

The ChatGPT API is a tool developed by OpenAI that allows developers to integrate the capabilities of GPT-3 and GPT-3.5-Turbo into their applications. The GPT-3 family of models is well-known for its ability to understand and generate natural language or even code.

GPT-3.5-Turbo, a more recent addition, has been optimized for chat-based tasks but works well for traditional completions tasks as well source.

GPT-4 is already out too:

Recommended: 10 High-IQ Things GPT-4 Can Do That GPT-3.5 Can’t

Key Features and Benefits

• Impressive language understanding: ChatGPT API is equipped with powerful natural language processing capabilities, enabling it to understand complex content, context, and generate meaningful responses.
• Efficiency: GPT-3.5-Turbo is designed to be more cost-effective than its predecessors within the GPT-3 family. This lower-cost option allows developers to build applications without compromising performance source.
• Customizability: The API allows developers to control parameters such as temperature, which affects the creativity and randomness of the generated output source.
• Versatility: ChatGPT API is suitable for a wide range of applications, including customer support, content generation, code generation, translations, and much more.

The ChatGPT API’s key features offer developers several benefits, such as providing powerful language models, enhanced efficiency, and increased customization options. Its compatibility with GPT-3.5-Turbo makes it an attractive choice for creating diverse applications, from customer service to code generation.

Temperature Parameter Explained

Here’s a great table that showcases some possible selections of the parameters Temperature and Top_p, both powerful meta parameters to control the model’s output performance (source):

Impact on Randomness

The temperature parameter is a part of the ChatGPT API and plays a significant role in controlling the randomness of the generated text. It is a hyperparameter that determines the level of unpredictability in the output.

The temperature value is a floating point number between 0 and 1. When the temperature is set to 0, the model will always choose the most likely token, resulting in consistent and predictable responses.

On the other hand, a temperature value of 1 will treat all tokens equally, producing a more diverse range of responses (source).

Creativity

The temperature parameter also affects the creativity of the ChatGPT API’s output. By fine-tuning this hyperparameter, you can control how “creative” or original the API’s responses are.

A higher temperature (e.g., 0.7) results in more diverse and creative output, whereas a lower temperature (e.g., 0.2) narrows down the output’s focus, making it more deterministic and topic-specific.

To summarize, the temperature parameter in the ChatGPT API allows you to control the randomness and creativity of the generated text, thereby influencing its diversity and originality. By adjusting this hyperparameter, you can achieve the desired levels of predictability and creativity depending on specific use cases and requirements.

Working with the API

When working with the ChatGPT API, one of the key aspects to consider is setting the temperature parameter, as it influences the creativity and determinism of the generated text.

Python

To interact with the ChatGPT API using Python, you will first need to generate your API keys by logging into your OpenAI account. Once you have the keys, you can install the required OpenAI Python library using pip:

pip install openai

Recommended: How to Install OpenAI in Python?

Import the library and set up the API key as follows:

import openai

openai.api_key = "your-api-key"

Initiate a conversation with the ChatGPT API using the required parameters, including the temperature:

response = openai.Completion.create(
    engine="text-davinci-003",
    prompt="Your prompt here",
    max_tokens=100,
    n=1,
    stop=None,
    temperature=0.7,  # Adjust this value based on your preferred creativity level
    top_p=1,
    frequency_penalty=0,
    presence_penalty=0,
)

A higher or lower temperature value can be used to control the level of creativity in the API’s response.

GitHub Libraries

There are also many GitHub libraries available to help interact with the ChatGPT API. Most libraries will require you to provide your API key and often include the option to set the temperature parameter.

Browse and select one suitable for your needs and programming language by searching for “ChatGPT API libraries” on GitHub.

For example, if you want to work with JavaScript, you can look into openai-js:

npm install openai-js

Here’s a sample code using the openai-js library to work with the API:

const openai = require("openai-js");

const apiKey =process.env.OPENAI_API_KEY;
openai.setup(apiKey);

const prompt = "Your prompt here";
const temperature = 0.7; // Adjust this value based on your preferred creativity level

openai.api.createCompletion(prompt, "text-davinci-003", temperature).then(res=> {
    console.log(res.choices[0].text);
});

Remember to be cautious and realistic with the temperature setting, as it can significantly impact the quality and relevance of the generated text when working with the ChatGPT API.

Fine-Tuning ChatGPT Responses

Here is a table that illustrates how different temperature settings might affect the output:

Please note that these are examples, and the actual output may vary each time the model is run, even with the same temperature setting. Also, the settings and behaviors might have been updated.

Top_P and Low-Probability Words

Top_P is an important parameter when fine-tuning ChatGPT responses. It filters out the low-probability words from the generated output. By adjusting the value of Top_P, you can achieve different levels of creativity in the generated text.

A higher value will include more low-probability words, leading to more diverse output. A lower value will result in a more focused and concise output by excluding low-probability words.

Consider the following example:

• With Top_P set to 0.9, the generated text might include a variety of words and phrases: ChatGPT is an amazing new technology that makes communication easier and more interactive by understanding natural language.
• With Top_P set to 0.5, the generated text could be more focused: ChatGPT is a helpful tool that improves communication by understanding text.

Deterministic vs. Predictable Behaviors

When working with ChatGPT, it’s essential to understand the differences between deterministic and predictable behaviors in the generated output. This will help you make informed decisions when adjusting the API parameters.

Deterministic behavior means that the output remains consistent, even with the same input. Lowering the temperature results in more deterministic output, as the generated text will closely resemble the input.

Predictable behavior refers to the extent that the output can be anticipated or forecasted based on the input, but not necessarily repeating the same text. Lowering Top_P can increase the predictability of output, as it filters out less likely words.

To strike a balance between deterministic and predictable behaviors, you can experiment with different combinations of temperature and Top_P. This helps ensure that your ChatGPT responses achieve an optimal balance of creativity, focus, and relevant information.

Temperature vs Top_P Parameters: What’s The Difference?

The temperature and top_p parameters in OpenAI’s models are both used to control the randomness of the model’s output, but they do it in slightly different ways:

1. Temperature: This parameter scales the logits (the output values before they are converted into probabilities) before the softmax operation during the prediction of the next token. A high temperature value (closer to 1) will make all words more equally likely, resulting in more diverse, but potentially less predictable and coherent output. A low temperature value (closer to 0) will make the output more focused and deterministic, as it will make the probabilities of the most likely words even higher.
2. Top_p (also known as nucleus sampling): Instead of sampling from the entire distribution, the model first discards a tail of less probable words so that the total probability mass of the remaining words is top_p (a value between 0 and 1). It then samples the next word from this reduced distribution. This method can increase diversity and avoid very unlikely predictions, without leading to as much randomness as high temperature settings.

For example, if top_p is set to 0.9, the model will narrow down the word options to a subset that collectively have 90% probability, and then pick randomly from that subset.

In practice, both parameters are often tuned to achieve a desirable balance between coherence and diversity in the output. Sometimes they are used together, allowing for nuanced control over the randomness of the generated text.

ChatCompletion

The ChatGPT API’s ChatCompletion() function serves as an interface to interact with GPT models like GPT-4, leveraging them for more natural-sounding conversations. It also supports API parameters like “temperature” that control the randomness or creativity of generated text (source).

Here’s an example from the docs:

# Note: you need to be using OpenAI Python v0.27.0 for the code below to work
import openai

openai.ChatCompletion.create(
  model="gpt-3.5-turbo",
  messages=[
        {"role": "system", "content": "You are a helpful assistant."},
        {"role": "user", "content": "Who won the world series in 2020?"},
        {"role": "assistant", "content": "The Los Angeles Dodgers won the World Series in 2020."},
        {"role": "user", "content": "Where was it played?"}
    ]
)

Higher temperatures result in more diverse outputs, while lower temperatures produce predictable and deterministic results. (source)

Future Developments and LLMs

The advancement of large language models (LLMs) like ChatGPT has been remarkable in recent years. As research in this field progresses, more sophisticated and diverse applications are expected to emerge.

With models like GPT-3.5-turbo and GPT-4, users have benefited from better language understanding and generation capabilities, as well as being cost-effective. The versatile nature of these models allows for their use in both chat-based and traditional completion tasks.

One area of focus for future developments involves refining the control of generated text.

For example, the ChatGPT API uses temperature as a hyperparameter to manage creativity and randomness in the output.

As LLMs evolve, it is expected that more refined controls will become available for users to fine-tune the generated content according to their specific needs.

As LLMs continue to improve, they are likely to demonstrate increased potential in various fields, such as education, history, mathematics, medicine, and physics. This would lead to a more widespread adoption of LLMs in both research and practical applications.

While GPT-3.5-turbo stands as a strong example of LLM evolution, it is speculated that future models such as ChatGPT-4 will house even more parameters, resulting in enhanced capabilities. As the models become more complex, the potential for innovative applications will continue to grow in response to the models’ increasing proficiency in understanding and generating text.

OpenAI Glossary Cheat Sheet (100% Free PDF Download)

Finally, check out our free cheat sheet on OpenAI terminology, many Finxters have told me they love it!

Recommended: OpenAI Terminology Cheat Sheet (Free Download PDF)

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If you are interested in natural language processing (NLP) and computer vision, you may have heard about MiniGPT-4.

This neural network model has been developed to improve vision-language comprehension by incorporating a frozen visual encoder and a frozen large language model (LLM) with a single projection layer.

MiniGPT-4 has demonstrated numerous capabilities similar to GPT-4, like generating detailed image descriptions and creating websites from handwritten drafts.

One of the most impressive features of MiniGPT-4 is its computation efficiency. Despite its advanced capabilities, this model is designed to be lightweight and easy to use. This makes it an ideal choice for developers who need to generate natural language descriptions of images but don’t want to spend hours training a complex neural network.

Image source: https://github.com/Vision-CAIR/MiniGPT-4

Additionally, MiniGPT-4 has been shown to have high generation reliability, meaning that it consistently produces accurate and relevant descriptions of images.

What is MiniGPT-4?

If you’re looking for a computationally efficient large language model that can generate reliable text, MiniGPT-4 might be the solution you’re looking for.

MiniGPT-4 is a language model architecture that combines a frozen visual encoder with a frozen large language model (LLM) using just one linear projection layer. The model is designed to align the visual features with the language model, making it capable of processing images alongside language.

Image source: https://github.com/Vision-CAIR/MiniGPT-4

MiniGPT-4 is an open-source model that can be fine-tuned to perform complex vision-language tasks like GPT-4. The model architecture consists of a vision encoder with a pre-trained ViT and Q-Former, a single linear projection layer, and an advanced Vicuna large language model. The trained checkpoint can be used for transfer learning, and the model can be fine-tuned on specific tasks with additional data.

MiniGPT-4 has many capabilities similar to those exhibited by GPT-4, including detailed image description generation and website creation from hand-written drafts.

Image Source: https://minigpt-4.github.io/

The model is computationally efficient and can be trained on a single GPU, making it accessible to researchers and developers who don’t have access to large-scale computing resources.

Video Example of Using MiniGPT

MiniGPT-4 Demo

If you’re interested in trying out MiniGPT-4, you’ll be pleased to know that a demo is available for you to test:

Demo Link: https://minigpt-4.github.io/

The demo allows you to see the capabilities of MiniGPT-4 in action and provides a glimpse of what you can expect if you decide to use it in your own projects.

User-Friendly Demo: The MiniGPT-4 demo is user-friendly and easy to use, even if you’re unfamiliar with this technology. The interface is simple and straightforward, allowing you to input text or images and see how MiniGPT-4 processes them. The demo is intuitive, so you can start immediately without prior knowledge or experience.

Generate Websites From Hand-Written Text: One of the most impressive features of the MiniGPT-4 demo is its ability to generate websites from handwritten text. This means you can input a piece of text, and MiniGPT-4 will create a website based on that text. The websites generated by MiniGPT-4 are professional-looking and can be used for various purposes.

Create Image Descriptions: MiniGPT-4 can also create detailed image descriptions in addition to generating websites. This is particularly useful for those who work in fields such as art or photography, where providing detailed descriptions of images is essential. With MiniGPT-4, you can input an image and receive a detailed description that accurately captures the essence of the image.

Image Source: https://minigpt-4.github.io/

MiniGPT-4 for Image-Text Pairs

Let’s explore how MiniGPT-4 can help you with image-text pairs.

Aligned Image-Text Pairs

MiniGPT-4 uses aligned image-text pairs to learn how to generate accurate descriptions of images. MiniGPT-4 aligns a frozen visual encoder with a frozen language model called Vicuna using just one projection layer during training.

This allows MiniGPT-4 to learn how to generate natural language descriptions of images aligned with the image’s visual features.

Raw Image-Text Pairs

MiniGPT-4 can also work with raw image-text pairs. However, the quality of the dataset is crucial for the performance of MiniGPT-4.

To achieve high accuracy, you need a high-quality dataset of image-text pairs. MiniGPT-4 requires a large and diverse dataset of high-quality image-text pairs to learn how to generate accurate descriptions of images.

Image Descriptions

MiniGPT-4 can generate accurate descriptions of images, write texts based on images, provide solutions to problems depicted in pictures, and even teach users how to do certain things based on photos. MiniGPT-4’s ability to generate accurate descriptions of images is due to its powerful visual encoder and ability to align the visual features with natural language descriptions.

Multi-Modal Abilities

MiniGPT-4 has demonstrated extraordinary multi-modal abilities, such as directly generating websites from handwritten text and identifying humorous elements within images. These features are rarely observed in previous vision-language models.

Image Source: https://minigpt-4.github.io/

Let’s take a closer look at some of MiniGPT-4’s multi-modal abilities:

Image Description Generation

MiniGPT-4 can generate descriptions of images.

For example, if you have an image of a product you want to sell online, you can use MiniGPT-4 to generate a description of the product you can use in your online store.

MiniGPT-4 can also be used to generate descriptions of images for people who are visually impaired. This can be particularly helpful for people who rely on screen readers to access information online.

Conversation Template

MiniGPT-4 can generate conversational templates. MiniGPT-4 can generate a template to use as a starting point for your conversation.

Examples:

• If you need to have a conversation with your boss about a difficult topic, you can use MiniGPT-4 to generate a template that you can use to start the conversation.
• MiniGPT-4 can also generate conversational templates for people struggling to express themselves verbally or with hand-written drafts.

Recommended: Free OpenAI Terminology Cheat Sheet (PDF)

MiniGPT-4 Implementation

Installation

You can install the code from the Vision-CAIR/MiniGPT-4 GitHub repository. The code is available under the BSD 3-Clause License. To install MiniGPT-4, clone the repository and install the required packages.

The installation instructions are provided in the README file of the repository:

git clone https://github.com/Vision-CAIR/MiniGPT-4.git
cd MiniGPT-4
conda env create -f environment.yml
conda activate minigpt4

Dataset Preparation

MiniGPT-4 requires aligned image-text pairs for training. The authors of MiniGPT-4 used the Laion and CC datasets for the first pretraining stage.

To prepare the datasets, download and preprocess them using the provided scripts. The instructions for dataset preparation are also available in the repository’s README file.

Model Config File

The model configuration file contains the hyperparameters and settings for the MiniGPT-4 model.

You can modify the configuration file to adjust the model settings according to your needs. The configuration file is provided in the repository and is named config.yaml.

The configuration file contains settings for the vision encoder, language model, training, and evaluation parameters.

Evaluation Config File

The evaluation configuration file contains the settings for evaluating the MiniGPT-4 model. You can modify the evaluation configuration file to adjust the evaluation settings according to your needs.

The evaluation configuration file is provided in the repository and is named eval.yaml. The evaluation configuration file contains settings for the evaluation dataset, the evaluation metrics, and the evaluation batch size.

MiniGPT-4 aligns a frozen visual encoder from BLIP-2 with a frozen LLM, Vicuna, using just one projection layer. The first traditional pretraining stage is trained using roughly 5 million aligned image-text pairs in 10 hours using 4 A100s.

After the first stage, Vicuna can understand the image. MiniGPT-4 is an implementation of the GPT architecture that enhances vision-language understanding by combining a frozen visual encoder with a frozen large language model (LLM) using just one projection layer.

The implementation is lightweight and requires training only the linear layer to align the visual features with the Vicuna.

Research Paper Citation

If you want to use this in your own research, use the following Latex template for citation:

@misc{zhu2022minigpt4,
      title={MiniGPT-4: Enhancing Vision-language Understanding with Advanced Large Language Models}, 
      author={Deyao Zhu and Jun Chen and Xiaoqian Shen and Xiang Li and Mohamed Elhoseiny},
      journal={arXiv preprint arXiv:2304.10592},
      year={2023},
}

Recommended: Free ChatGPT Prompting Cheat Sheet (PDF)

The post MiniGPT-4: The Latest Breakthrough in Language Generation Technology appeared first on Be on the Right Side of Change.