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🐸💬 - a deep learning toolkit for Text-to-Speech, battle-tested in research and production
Pretrain, finetune ANY AI model of ANY size on 1 or 10,000+ GPUs with zero code changes.
Code and documentation to train Stanford's Alpaca models, and generate the data.
Interactive deep learning book with multi-framework code, math, and discussions. Adopted at 500 universities from 70 countries including Stanford, MIT, Harvard, and Cambridge.
100-Days-Of-ML-Code中文版
ARIS ⚔️ (Auto-Research-In-Sleep) — Lightweight Markdown-only skills for autonomous ML research: cross-model review loops, idea discovery, and experiment automation. No framework, no lock-in — works with Claude Code, Codex, OpenClaw, or any LLM agent.
pix2tex: Using a ViT to convert images of equations into LaTeX code.
Transfer learning / domain adaptation / domain generalization / multi-task learning etc. Papers, codes, datasets, applications, tutorials.-迁移学习
Code for loralib, an implementation of "LoRA: Low-Rank Adaptation of Large Language Models"
1 Line of code data quality profiling & exploratory data analysis for Pandas and Spark DataFrames.
pix2code: Generating Code from a Graphical User Interface Screenshot
Low-code framework for building custom LLMs, neural networks, and other AI models
Fast and Accurate ML in 3 Lines of Code
Efficiently computes derivatives of NumPy code.
Easily train your own text-generating neural network of any size and complexity on any text dataset with a few lines of code.
Source code of PyGAD, a Python 3 library for building the genetic algorithm and training machine learning algorithms (Keras & PyTorch).
Deep probabilistic analysis of single-cell and spatial omics data
A deep learning toolkit for healthcare AI: EHR (MIMIC, eICU, OMOP), clinical text, signals and imaging. 30+ models, reproducible pipelines.
World's first general purpose 3D object detection codebse.
Code for "Social GAN: Socially Acceptable Trajectories with Generative Adversarial Networks", Gupta et al, CVPR 2018
DNABERT: pre-trained Bidirectional Encoder Representations from Transformers model for DNA-language in genome
Don't memorize ML. Understand it from first principles including Inference, Cuda, Deep Learning, Data Science and Reinforcement Learning
Deep and Machine Learning for Microscopy