Transformer vs RNN: Women in Red Dresses (Attention Is All They Need?)

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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

Python Repeat String Until Length

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The multiplication operator (*) allows you to repeat a given string a certain number of times. However, if you want to repeat a string to a specific length, you might need to employ a different approach, such as string slicing or a while loop. For example, while manipulating strings in Python, you may need to … Read more

Scalable Graph Partitioning for Distributed Graph Processing

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I just realized that the link to my doctoral thesis doesn’t work, so I decided to host it on the Finxter blog as a backup. Find the thesis here: 🔗 PDF Download link: https://blog.finxter.com/wp-content/uploads/2023/09/dissertation_christian_mayer_distributed_graph_processing_DIS-2019-03.pdf Here’s the abstract: 💡 Abstract: Distributed graph processing systems such as Pregel, PowerGraph, or GraphX have gained popularity due to their … Read more

Want Exploding Bitcoin Prices North of $500,000 per BTC? “Grow N” Says Metcalfe’s Law

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Metcalfe’s law states that the value of a network (V) is proportional to the square of the number of connected users of the system (N²). In this article, I’ll develop a Bitcoin price prediction (V) in the year 2030 based on the average growth rate of the number of Bitcoin nodes (N). General Intuition Metcalfe’s … Read more

What’s the Relation between Polygon and ETH

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As you dive into the crypto ecosystem, you may come across Polygon (MATIC) and Ethereum (ETH), two popular and interconnected projects. What’s the relationship of those two projects and tokens? Polygon, formerly known as Matic Network, is an interoperability and scaling framework designed for building Ethereum-compatible blockchains. Its native token, MATIC, serves multiple purposes, including … Read more

Top 10 LLM Training Datasets – It’s Money Laundering for Copyrighted Data!

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I’ve read the expression of large language models (LLMs) being “Money Laundering for Copyrighted Data” on Simon Willison’s blog. In today’s article, I’ll show you which exact training data sets open-source LLMs use, so we can gain some more insights into this new alien technology and, hopefully, get smarter and more effective prompters. Let’s get … Read more

Python Multiprocessing Pool [Ultimate Guide]

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Python Multiprocessing Fundamentals 🚀 Python’s multiprocessing module provides a simple and efficient way of using parallel programming to distribute the execution of your code across multiple CPU cores, enabling you to achieve faster processing times. By using this module, you can harness the full power of your computer’s resources, thereby improving your code’s efficiency. To … Read more

OpenAI API Functions & Embeddings Course (7/7): Sentiment Analysis using Embeddings

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💡 Full Course with Videos and Course Certificate (PDF): https://academy.finxter.com/university/openai-api-function-calls-and-embeddings/ Course Overview Welcome back to the final part of this tutorial series. In this part, we’ll be looking at simple sentiment analysis using embeddings. For most text classification tasks, fine-tuned machine learning models will do better than embeddings, because they have been meticulously tuned and … Read more

OpenAI API Functions & Embeddings Course (6/7): Similarity Comparison with Embeddings

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💡 Full Course with Videos and Course Certificate (PDF): https://academy.finxter.com/university/openai-api-function-calls-and-embeddings/ Course Overview Welcome back to part 6 of this tutorial series. In this part, we’re going to be looking at embeddings. What is an embedding? Without getting into algorithmic arithmetic an embedding is basically a numerical (vector) representation of text that makes no sense to … Read more

OpenAI API Functions & Embeddings Course (5/7): Calling Functions That Do Not Exist to Extract Structured Data

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💡 Full Course with Videos and Course Certificate (PDF): https://academy.finxter.com/university/openai-api-function-calls-and-embeddings/ Course Overview Welcome back to part 5, Where we’ll be calling functions that do not even exist. We’ll also briefly touch on pitfalls, failures, and retrying to make our code more robust as introducing AI tends to make our code a little more unpredictable and … Read more

OpenAI API Functions & Embeddings Course (4/7): Database Querying using ChatGPT

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💡 Full Course with Videos and Course Certificate (PDF): https://academy.finxter.com/university/openai-api-function-calls-and-embeddings/ Course Overview Welcome back to part 4, where we’ll be doing something mind-blowing yet again! In this part, we will be connecting ChatGPT to a database and then using ChatGPT to extract information from the database using only natural language on the end user’s part. … Read more

OpenAI API Functions & Embeddings Course (3/7): Multiple Functions and Multiple Calls

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💡 Full Course with Videos and Course Certificate (PDF): https://academy.finxter.com/university/openai-api-function-calls-and-embeddings/ Course Overview Welcome back to part 3, where we’ll take things one step further and look at having multiple functions and even calling multiple functions in a row. First, let’s make a second function for ChatGPT to call. We have ChatGPT with access to the … Read more