January 30, 2023 In this post, we'll implement a GPT from scratch in just 60 lines of numpy. We'll then load the trained GPT-2 model weights released by OpenAI into our implementation and generate some text. Note: This post assumes familiarity with Python, NumPy, and some basic experience with neural networks. This implementation is for educational purposes, so it's missing lots of features/improv
MAI-Thinking-1: Building a Hill-Climbing Machine The Microsoft AI Team 1 Abstract Progress in AI is driven not by a single model, but by the ability to continually improve upon the current state of models. Achieving this requires treating model development as a system-level optimization problem, for which the solution is building a hill-climbing machine for rapid improvement. Our process includes
17 types of similarity and dissimilarity measures used in data science. | Towards Data Science
The following article explains various methods for computing distances and showing their instances in our daily lives. Additionally, it… Various ML metrics. Inspired by Maarten Grootendorst. "There is no Royal Road to Geometry." – Euclid Quick note: Everything written and visualized has been created by the author unless it was specified. Illustrations and equations were generated using tools like
Exactly ten years ago today, we published "Why We Created Julia", introducing the Julia project to the world. At this point, we have moved well past the ambitious goals set out in the original blog post. Julia is now used by hundreds of thousands of people. It is taught at hundreds of universities and entire companies are being formed that build their software stacks on Julia. From personalized me
Version 1.0
Version 1.0# For a short description of the main highlights of the release, please refer to Release Highlights for scikit-learn 1.0. Legend for changelogs Major Feature something big that you couldn’t do before. Feature something that you couldn’t do before. Efficiency an existing feature now may not require as much computation or memory. Enhancement a miscellaneous minor improvement. Fix somethin
We have seen that Python language is a powerful tool for data science and data operations, but how powerful is Python for Data visualization? One of the key responsibilities of Data scientists is to communicate results effectively with the stakeholders. This is where the power of visualization comes into play. Creating effective visualizations helps businesses identify patterns and subsequently he
Essential Machine Learning Equations: A Reference Guide
Why This Guide Exists I created this as a practical reference for the mathematical foundations of machine learning. It’s not comprehensive (no guide could be), but it covers equations I find myself returning to regularly. Each section includes working Python implementations that I’ve tested or used at some point. This started from a tweet by @goyal__pramod and grew as I collected formulas I actual
Paul: Today’s spotlight: Paolo Perrone, master of turning tech into scroll-stopping content. This one’s packed, let’s go 👀 ↓ I’m going to be honest with you. Most AI agent tutorials are garbage. They show you how to copy-paste LangChain code, build a demo that breaks the moment you try anything real, and leave you feeling like you learned something. Three months later, you try to build something
j次のブックマーク
k前のブックマーク
lあとで読む
eコメント一覧を開く
oページを開く
GPT in 60 Lines of NumPy | Jay Mody
MAI-Thinking-1: Building a Hill-Climbing Machine
Why We Use Julia, 10 Years Later
Data Visualization Using Python
The Realistic Guide to Mastering AI Agents in 2026