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 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
💡 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
💡 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
💡 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
💡 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
💡 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
💡 Full Course with Videos and Course Certificate (PDF): https://academy.finxter.com/university/openai-api-function-calls-and-embeddings/ Course Overview Welcome to the second part of the tutorial, where we’ll look at the shortcomings of ChatGPT and use our functions with parameters to overcome them. First, make a file in your base directory called ‘Ba_chatgpt_no_weather.py‘ and copy/paste the following basic code into it: … Read more
💡 Full Course with Videos and Course Certificate (PDF): https://academy.finxter.com/university/openai-api-function-calls-and-embeddings/ Course Overview 👋 Hi, and welcome. I’m Dirk van Meerveld, and I’ll be your host and guide for this tutorial series, where we’ll be focussing on OpenAi’s ChatGPT function calls and the embeddings API. Function calls will allow us to make ChatGPT even smarter by … Read more
To convert MIDI to MP3 in Python, two great ways is using the pydub and fluidsynth libraries: Here are three easy steps to convert MIDI to MP3 in Python: 🎵 Step 1: Install the pydub and fluidsynth libraries: You also need to install fluidsynth (see below, keep reading this article). The installation process for fluidsynth … Read more
Using heapq.nlargest() and heapq.nsmallest() is more efficient than sorting the entire list and then slicing it. Sorting takes O(n log n) time and slicing takes O(N) time, making the overall time complexity O(n log n) + O(N). However, heapq.nlargest() and heapq.nsmallest() have a time complexity of O(n log N), which is more efficient, especially when … Read more
Wow, Tesla Full Self-Driving (FSD), the most incredible real-world software system humanity has ever seen, doesn’t rely on explicit code. It’s an end-to-end neural network solution. The new paradigm is: there is no explicit coding; the code is … neural net weights trained on massive data! 🤯 In a recent live stream, Elon Musk and … Read more