I assume you’re a human reader even though the apriori probability is not on my side on this assumption. As a human, you need calories to power your daily life. But if you have enough calories, air, and water, you can survive everywhere, develop, and figure out the small and big problems. If you’re powered … Read more
I spent the last couple of hours scouring the web for free LLM books that are not trash, scam, or outright malicious links. This curated list is the proud result. Have fun reading! 🥸👇 What Is ChatGPT Doing … and Why Does It Work? by Stephen Wolfram 📖 Description: “Nobody expected this—not even its creators: … Read more
Huawei’s Pangu-Weather AI model represents a significant advancement in weather forecasting. This model is the first AI prediction model to outperform traditional numerical weather forecast methods in terms of accuracy and speed. It processes data 10,000 times faster than conventional methods, reducing the global weather prediction time to just seconds. Note that this is not … Read more
First, we know very little about Q* or Q Star, and the little we know (publicly) draws from multiple sources and involves a lot of speculation, so take everything in this article with a grain of salt. With this disclaimer out of the way, let’s dive right in: Where Does the Name Q* (Q-Star) Come … Read more
💡 This Llama 2 Prompt Engineering course helps you stay on the right side of change. Our course is meticulously designed to provide you with hands-on experience through genuine projects. 🔗 Prompt Engineering with Llama 2: Four Practical Projects using Python, Langchain, and Pinecone You’ll delve into practical applications such as book PDF querying, payroll auditing, and … Read more
Welcome to the ultimate video guide on the groundbreaking book “Deep Learning” by Ian Goodfellow, Yoshua Bengio, and Aaron Courville! 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. … Read more
🧑💻 10 Steps Learning Path: Do you want to learn Python but are unsure where to start? I’ve curated an optimal learning path, taking you from a complete beginner to an advanced Python programmer using Finxter Academy courses with downloadable PDF certificates. All courses can be freely accessed by Finxter Premium Members (no restrictions). Level … Read more
Llama 2 is the next generation of our open-source large language model. It is available for free for research and commercial use. Inside the Llama 2 model, you’ll find pretrained and fine-tuned language models like Llama Chat and Code Llama. These models range from 7B to 70B parameters and have been trained on 2 trillion … Read more
In this guide, let’s delve into preparing a dataset of Wikipedia articles for search, utilizing the OpenAI API. The process involves In case you need a quick refresher on OpenAI embeddings, feel free to read through our Finxter article here: 👇 💡 Recommended: What Are Embeddings in OpenAI? Prerequisites Before starting, ensure that you have … Read more
Both Python and Go are extremely relevant today. Both languages are widely used. Golang fans argue that people flock from Python to the newer and faster language Golang, also known as Go. Is this true? Not really. See this search volume comparison from Google Trends: 👇 Go is a much newer language than Python, released … Read more
An autoencoder is a neural network that learns to compress and reconstruct unlabeled data. It has two parts: an encoder that processes the input, and a decoder that reproduces it. While the original transformer model was an autoencoder with both encoder and decoder, OpenAI’s GPT series uses only a decoder. In a way, transformers are … Read more
TL;DR: Transformers process input sequences in parallel, making them computationally efficient compared to RNNs which operate sequentially. Both handle sequential data like natural language, but Transformers don’t require data to be processed in order. They avoid recursion, capturing word relationships through multi-head attention and positional embeddings. However, traditional Transformers can only capture dependencies within their … Read more