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机器学习
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- 维基百科
Machine learning is the practice of teaching a computer to learn. The concept uses pattern recognition, as well as other forms of predictive algorithms, to make judgments on incoming data. This field is closely related to artificial intelligence and computational statistics.
An Open Source Machine Learning Framework for Everyone
🤗 Transformers: the model-definition framework for state-of-the-art machine learning models in text, vision, audio, and multimodal models, for both inference and training.
12 weeks, 26 lessons, 52 quizzes, classic Machine Learning for all
The fastest path to AI-powered full stack observability, even for lean teams.
List of Computer Science courses with video lectures.
Tesseract Open Source OCR Engine (main repository)
Implement a ChatGPT-like LLM in PyTorch from scratch, step by step
The Patterns of Scalable, Reliable, and Performant Large-Scale Systems
Deep Learning for humans
🧑🏫 60+ Implementations/tutorials of deep learning papers with side-by-side notes 📝; including transformers (original, xl, switch, feedback, vit, ...), optimizers (adam, adabelief, sophia, ...), gans(cyclegan, stylegan2, ...), 🎮 reinforcement learning (ppo, dqn), capsnet, distillation, ... 🧠
Course to get into Large Language Models (LLMs) with roadmaps and Colab notebooks.
The world's simplest facial recognition api for Python and the command line
YOLOv5 🚀 in PyTorch > ONNX > CoreML > TFLite
Deepfakes Software For All
Financial data platform for analysts, quants and AI agents.
The Julia Programming Language