An elegant PyTorch deep reinforcement learning library.

🎆Interactive Online Platform that Visualizes Algorithms from Code

improve Llama-2's proficiency in comprehension, generation, and translation of Chinese.

推荐系统入门教程，在线阅读地址：https://datawhalechina.github.io/fun-rec/

🤗 The largest hub of ready-to-use datasets for AI models with fast, easy-to-use and efficient data manipulation tools

中文LLaMA&Alpaca大语言模型+本地CPU/GPU训练部署 (Chinese LLaMA & Alpaca LLMs)

Source code for the X Recommendation Algorithm

Instruct-tune LLaMA on consumer hardware

🤗 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.

Making large AI models cheaper, faster and more accessible

C++ 资源大全中文版，标准库、Web应用框架、人工智能、数据库、图片处理、机器学习、日志、代码分析等。由「开源前哨」和「CPP开发者」微信公号团队维护更新。

An adversarial example library for constructing attacks, building defenses, and benchmarking both

Learning Convolutional Neural Networks with Interactive Visualization.

📈 如何用深度强化学习自动炒股

A reimplementation of "The Lottery Ticket Hypothesis" (Frankle and Carbin) on MNIST.

yolov3 yolov4 channel and layer pruning, Knowledge Distillation 层剪枝，通道剪枝，知识蒸馏

Code for "Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks"

CTR prediction models based on deep learning(基于深度学习的广告推荐CTR预估模型)

A complete computer science study plan to become a software engineer.

An Automatic Model Compression (AutoMC) framework for developing smaller and faster AI applications.

Quantized Neural Network PACKage - mobile-optimized implementation of quantized neural network operators

Open Machine Learning Compiler Framework

ResNet model in TensorFlow

Generic and easy-to-use serving service for machine learning models

using c++ code to show the example of machine learning

A Tensorflow implementation of CapsNet(Capsules Net) in paper Dynamic Routing Between Capsules

Models built with TensorFlow

Integrate TensorFlow with CMake projects effortlessly

TensorFlow Tutorial and Examples for Beginners (support TF v1 & v2)

TensorFlow tutorials and best practices.