Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Core deep-learning frameworks and libraries for pretraining and distributed training.
Signals: deep-learning, neural-network, pytorch, tensorflow, jax, distributed-training, training, deepspeed
2,672 results
Multi-class confusion matrix library in Python
Python package for AutoML on Tabular Data with Feature Engineering, Hyper-Parameters Tuning, Explanations and Automatic Documentation
Sentiment Analysis, Text Classification, Text Augmentation, Text Adversarial defense, etc.;
The Art of Debugging Open Book
Hummingbird compiles trained ML models into tensor computation for faster inference.
This repository contains demos I made with the Transformers library by HuggingFace.
Learn how to develop, deploy and iterate on production-grade ML applications.
Collection of eclectic utils for python.
fit piecewise linear data for a specified number of line segments
:twisted_rightwards_arrows: Neural Network (NN) Streamer, Stream Processing Paradigm for Neural Network Apps/Devices.
🔥机器学习/深度学习/Python/大模型/多模态/LLM/deeplearning/Python/Algorithm interview/NLP Tutorial
Probabilistic reasoning and statistical analysis in TensorFlow
An implementation of the BERT model and its related downstream tasks based on the PyTorch framework. @跟我学机器学习
Detailed python notes & code for lectures and exercises of Andrej Karpathy's course "Neural Networks: Zero to Hero." The course is focused on building neural networks from scratch.
Deep Learning based NLP modeling for Russian language
DiffusionBlocks: Block-wise Neural Network Training via Diffusion Interpretation
Companion code for Machine Learning From Scratch — 10 core ML algorithms built from scratch with NumPy, compared with Scikit-learn and PyTorch.
RNNSharp is a toolkit of deep recurrent neural network which is widely used for many different kinds of tasks, such as sequence labeling, sequence-to-sequence and so on. It's written by C# language and based on .NET framework 4.6 or above versions. RNNSharp supports many different types of networks, such as forward and bi-directional network, sequence-to-sequence network, and different types of layers, such as LSTM, Softmax, sampled Softmax and others.
利用Pytorch设计完成的基于卷积神经网络实现的面部表情识别项目 —— A facial expression recognition project based on convolution neural network designed by Pytorch 【Plus版本】：https://github.com/hexiang10/face-recognition-plus
This repository contains a Pytorch implementation of the paper "The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks" by Jonathan Frankle and Michael Carbin that can be easily adapted to any model/dataset.
Awesome Deep Learning for Time-Series Imputation, including an unmissable paper and tool list about applying neural networks to impute incomplete time series containing NaN missing values/data
Decision Trees, Random Forest, Dynamic Time Warping, Naive Bayes, KNN, Linear Regression, Logistic Regression, Mixture Of Gaussian, Neural Network, PCA, SVD, Gaussian Naive Bayes, Fitting Data to Gaussian, K-Means
NiuTensor is an open-source toolkit developed by a joint team from NLP Lab. at Northeastern University and the NiuTrans Team. It provides tensor utilities to create and train neural networks.
A brain-inspired version of generative replay for continual learning with deep neural networks (e.g., class-incremental learning on CIFAR-100; PyTorch code).
24,540 repositories in the index in total.
