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TensorFlow-Examples

# TensorFlow Examples This tutorial was designed for easily diving into TensorFlow, through examples. For readability, it includes both notebooks and source codes with explanation, for both TF v1 & v2. It is suitable for beginners who want to find clear and concise examples about TensorFlow. Besides the traditional 'raw' TensorFlow implementations, you can also find the latest TensorFlow API practices (such as `layers`, `estimator`, `dataset`, ...). **Update (05/16/2020):** Moving all default examples to TF2. For TF v1 examples: [check here](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1). ## Tutorial index #### 0 - Prerequisite - [Introduction to Machine Learning](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/0_Prerequisite/ml_introduction.ipynb). - [Introduction to MNIST Dataset](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/0_Prerequisite/mnist_dataset_intro.ipynb). #### 1 - Introduction - **Hello World** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/1_Introduction/helloworld.ipynb)). Very simple example to learn how to print "hello world" using TensorFlow 2.0+. - **Basic Operations** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/1_Introduction/basic_operations.ipynb)). A simple example that cover TensorFlow 2.0+ basic operations. #### 2 - Basic Models - **Linear Regression** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/2_BasicModels/linear_regression.ipynb)). Implement a Linear Regression with TensorFlow 2.0+. - **Logistic Regression** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/2_BasicModels/logistic_regression.ipynb)). Implement a Logistic Regression with TensorFlow 2.0+. - **Word2Vec (Word Embedding)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/2_BasicModels/word2vec.ipynb)). Build a Word Embedding Model (Word2Vec) from Wikipedia data, with TensorFlow 2.0+. - **GBDT (Gradient Boosted Decision Trees)** ([notebooks](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/2_BasicModels/gradient_boosted_trees.ipynb)). Implement a Gradient Boosted Decision Trees with TensorFlow 2.0+ to predict house value using Boston Housing dataset. #### 3 - Neural Networks ##### Supervised - **Simple Neural Network** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/3_NeuralNetworks/neural_network.ipynb)). Use TensorFlow 2.0 'layers' and 'model' API to build a simple neural network to classify MNIST digits dataset. - **Simple Neural Network (low-level)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/3_NeuralNetworks/neural_network_raw.ipynb)). Raw implementation of a simple neural network to classify MNIST digits dataset. - **Convolutional Neural Network** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/3_NeuralNetworks/convolutional_network.ipynb)). Use TensorFlow 2.0+ 'layers' and 'model' API to build a convolutional neural network to classify MNIST digits dataset. - **Convolutional Neural Network (low-level)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/3_NeuralNetworks/convolutional_network_raw.ipynb)). Raw implementation of a convolutional neural network to classify MNIST digits dataset. - **Recurrent Neural Network (LSTM)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/3_NeuralNetworks/recurrent_network.ipynb)). Build a recurrent neural network (LSTM) to classify MNIST digits dataset, using TensorFlow 2.0 'layers' and 'model' API. - **Bi-directional Recurrent Neural Network (LSTM)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/3_NeuralNetworks/bidirectional_rnn.ipynb)). Build a bi-directional recurrent neural network (LSTM) to classify MNIST digits dataset, using TensorFlow 2.0+ 'layers' and 'model' API. - **Dynamic Recurrent Neural Network (LSTM)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/3_NeuralNetworks/dynamic_rnn.ipynb)). Build a recurrent neural network (LSTM) that performs dynamic calculation to classify sequences of variable length, using TensorFlow 2.0+ 'layers' and 'model' API. ##### Unsupervised - **Auto-Encoder** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/3_NeuralNetworks/autoencoder.ipynb)). Build an auto-encoder to encode an image to a lower dimension and re-construct it. - **DCGAN (Deep Convolutional Generative Adversarial Networks)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/3_NeuralNetworks/dcgan.ipynb)). Build a Deep Convolutional Generative Adversarial Network (DCGAN) to generate images from noise. #### 4 - Utilities - **Save and Restore a model** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/4_Utils/save_restore_model.ipynb)). Save and Restore a model with TensorFlow 2.0+. - **Build Custom Layers & Modules** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/4_Utils/build_custom_layers.ipynb)). Learn how to build your own layers / modules and integrate them into TensorFlow 2.0+ Models. - **Tensorboard** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/4_Utils/tensorboard.ipynb)). Track and visualize neural network computation graph, metrics, weights and more using TensorFlow 2.0+ tensorboard. #### 5 - Data Management - **Load and Parse data** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/5_DataManagement/load_data.ipynb)). Build efficient data pipeline with TensorFlow 2.0 (Numpy arrays, Images, CSV files, custom data, ...). - **Build and Load TFRecords** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/5_DataManagement/tfrecords.ipynb)). Convert data into TFRecords format, and load them with TensorFlow 2.0+. - **Image Transformation (i.e. Image Augmentation)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/5_DataManagement/image_transformation.ipynb)). Apply various image augmentation techniques with TensorFlow 2.0+, to generate distorted images for training. #### 6 - Hardware - **Multi-GPU Training** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/6_Hardware/multigpu_training.ipynb)). Train a convolutional neural network with multiple GPUs on CIFAR-10 dataset. ## TensorFlow v1 The tutorial index for TF v1 is available here: [TensorFlow v1.15 Examples](tensorflow_v1). Or see below for a list of the examples. ## Dataset Some examples require MNIST dataset for training and testing. Don't worry, this dataset will automatically be downloaded when running examples. MNIST is a database of handwritten digits, for a quick description of that dataset, you can check [this notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/0_Prerequisite/mnist_dataset_intro.ipynb). Official Website: [http://yann.lecun.com/exdb/mnist/](http://yann.lecun.com/exdb/mnist/). ## Installation To download all the examples, simply clone this repository: ``` git clone https://github.com/aymericdamien/TensorFlow-Examples ``` To run them, you also need the latest version of TensorFlow. To install it: ``` pip install tensorflow ``` or (with GPU support): ``` pip install tensorflow_gpu ``` For more details about TensorFlow installation, you can check [TensorFlow Installation Guide](https://www.tensorflow.org/install/) ## TensorFlow v1 Examples - Index The tutorial index for TF v1 is available here: [TensorFlow v1.15 Examples](tensorflow_v1). #### 0 - Prerequisite - [Introduction to Machine Learning](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/0_Prerequisite/ml_introduction.ipynb). - [Introduction to MNIST Dataset](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/0_Prerequisite/mnist_dataset_intro.ipynb). #### 1 - Introduction - **Hello World** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/notebooks/1_Introduction/helloworld.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/examples/1_Introduction/helloworld.py)). Very simple example to learn how to print "hello world" using TensorFlow. - **Basic Operations** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/1_Introduction/basic_operations.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-examples/Examples/blob/master/tensorflow_v1/1_Introduction/basic_operations.py)). A simple example that cover TensorFlow basic operations. - **TensorFlow Eager API basics** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/notebooks/1_Introduction/basic_eager_api.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/examples/1_Introduction/basic_eager_api.py)). Get started with TensorFlow's Eager API. #### 2 - Basic Models - **Linear Regression** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/notebooks/2_BasicModels/linear_regression.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/examples/2_BasicModels/linear_regression.py)). Implement a Linear Regression with TensorFlow. - **Linear Regression (eager api)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/notebooks/2_BasicModels/linear_regression_eager_api.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/examples/2_BasicModels/linear_regression_eager_api.py)). Implement a Linear Regression using TensorFlow's Eager API. - **Logistic Regression** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/notebooks/2_BasicModels/logistic_regression.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/examples/2_BasicModels/logistic_regression.py)). Implement a Logistic Regression with TensorFlow. - **Logistic Regression (eager api)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/notebooks/2_BasicModels/logistic_regression_eager_api.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/examples/2_BasicModels/logistic_regression_eager_api.py)). Implement a Logistic Regression using TensorFlow's Eager API. - **Nearest Neighbor** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/notebooks/2_BasicModels/nearest_neighbor.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/examples/2_BasicModels/nearest_neighbor.py)). Implement Nearest Neighbor algorithm with TensorFlow. - **K-Means** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/notebooks/2_BasicModels/kmeans.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/examples/2_BasicModels/kmeans.py)). Build a K-Means classifier with TensorFlow. - **Random Forest** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/notebooks/2_BasicModels/random_forest.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/examples/2_BasicModels/random_forest.py)). Build a Random Forest classifier with TensorFlow. - **Gradient Boosted Decision Tree (GBDT)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/notebooks/2_BasicModels/gradient_boosted_decision_tree.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/examples/2_BasicModels/gradient_boosted_decision_tree.py)). Build a Gradient Boosted Decision Tree (GBDT) with TensorFlow. - **Word2Vec (Word Embedding)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/notebooks/2_BasicModels/word2vec.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/examples/2_BasicModels/word2vec.py)). Build a Word Embedding Model (Word2Vec) from Wikipedia data, with TensorFlow. #### 3 - Neural Networks ##### Supervised - **Simple Neural Network** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/3_NeuralNetworks/notebooks/neural_network_raw.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/examples/3_NeuralNetworks/neural_network_raw.py)). Build a simple neural network (a.k.a Multi-layer Perceptron) to classify MNIST digits dataset. Raw TensorFlow implementation. - **Simple Neural Network (tf.layers/estimator api)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/notebooks/3_NeuralNetworks/neural_network.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/examples/3_NeuralNetworks/neural_network.py)). Use TensorFlow 'layers' and 'estimator' API to build a simple neural network (a.k.a Multi-layer Perceptron) to classify MNIST digits dataset. - **Simple Neural Network (eager api)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/notebooks/3_NeuralNetworks/neural_network_eager_api.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/examples/3_NeuralNetworks/neural_network_eager_api.py)). Use TensorFlow Eager API to build a simple neural network (a.k.a Multi-layer Perceptron) to classify MNIST digits dataset. - **Convolutional Neural Network** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/notebooks/3_NeuralNetworks/convolutional_network_raw.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/examples/3_NeuralNetworks/convolutional_network_raw.py)). Build a convolutional neural network to classify MNIST digits dataset. Raw TensorFlow implementation. - **Convolutional Neural Network (tf.layers/estimator api)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/notebooks/3_NeuralNetworks/convolutional_network.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/examples/3_NeuralNetworks/convolutional_network.py)). Use TensorFlow 'layers' and 'estimator' API to build a convolutional neural network to classify MNIST digits dataset. - **Recurrent Neural Network (LSTM)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/notebooks/3_NeuralNetworks/recurrent_network.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/examples/3_NeuralNetworks/recurrent_network.py)). Build a recurrent neural network (LSTM) to classify MNIST digits dataset. - **Bi-directional Recurrent Neural Network (LSTM)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/notebooks/3_NeuralNetworks/bidirectional_rnn.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/examples/3_NeuralNetworks/bidirectional_rnn.py)). Build a bi-directional recurrent neural network (LSTM) to classify MNIST digits dataset. - **Dynamic Recurrent Neural Network (LSTM)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/notebooks/3_NeuralNetworks/dynamic_rnn.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/examples/3_NeuralNetworks/dynamic_rnn.py)). Build a recurrent neural network (LSTM) that performs dynamic calculation to classify sequences of different length. ##### Unsupervised - **Auto-Encoder** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/notebooks/3_NeuralNetworks/autoencoder.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/examples/3_NeuralNetworks/autoencoder.py)). Build an auto-encoder to encode an image to a lower dimension and re-construct it. - **Variational Auto-Encoder** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/notebooks/3_NeuralNetworks/variational_autoencoder.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/examples/3_NeuralNetworks/variational_autoencoder.py)). Build a variational auto-encoder (VAE), to encode and generate images from noise. - **GAN (Generative Adversarial Networks)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/notebooks/3_NeuralNetworks/gan.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/examples/3_NeuralNetworks/gan.py)). Build a Generative Adversarial Network (GAN) to generate images from noise. - **DCGAN (Deep Convolutional Generative Adversarial Networks)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/notebooks/3_NeuralNetworks/dcgan.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/examples/3_NeuralNetworks/dcgan.py)). Build a Deep Convolutional Generative Adversarial Network (DCGAN) to generate images from noise. #### 4 - Utilities - **Save and Restore a model** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/notebooks/4_Utils/save_restore_model.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/examples/4_Utils/save_restore_model.py)). Save and Restore a model with TensorFlow. - **Tensorboard - Graph and loss visualization** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/notebooks/4_Utils/tensorboard_basic.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/examples/4_Utils/tensorboard_basic.py)). Use Tensorboard to visualize the computation Graph and plot the loss. - **Tensorboard - Advanced visualization** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/notebooks/4_Utils/tensorboard_advanced.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/examples/4_Utils/tensorboard_advanced.py)). Going deeper into Tensorboard; visualize the variables, gradients, and more... #### 5 - Data Management - **Build an image dataset** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/notebooks/5_DataManagement/build_an_image_dataset.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/examples/5_DataManagement/build_an_image_dataset.py)). Build your own images dataset with TensorFlow data queues, from image folders or a dataset file. - **TensorFlow Dataset API** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/notebooks/5_DataManagement/tensorflow_dataset_api.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/examples/5_DataManagement/tensorflow_dataset_api.py)). Introducing TensorFlow Dataset API for optimizing the input data pipeline. - **Load and Parse data** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/notebooks/5_DataManagement/load_data.ipynb)). Build efficient data pipeline (Numpy arrays, Images, CSV files, custom data, ...). - **Build and Load TFRecords** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/notebooks/5_DataManagement/tfrecords.ipynb)). Convert data into TFRecords format, and load them. - **Image Transformation (i.e. Image Augmentation)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/notebooks/5_DataManagement/image_transformation.ipynb)). Apply various image augmentation techniques, to generate distorted images for training. #### 6 - Multi GPU - **Basic Operations on multi-GPU** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/notebooks/6_MultiGPU/multigpu_basics.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/examples/6_MultiGPU/multigpu_basics.py)). A simple example to introduce multi-GPU in TensorFlow. - **Train a Neural Network on multi-GPU** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/notebooks/6_MultiGPU/multigpu_cnn.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/examples/6_MultiGPU/multigpu_cnn.py)). A clear and simple TensorFlow implementation to train a convolutional neural network on multiple GPUs. ## More Examples The following examples are coming from [TFLearn](https://github.com/tflearn/tflearn), a library that provides a simplified interface for TensorFlow. You can have a look, there are many [examples](https://github.com/tflearn/tflearn/tree/master/examples) and [pre-built operations and layers](http://tflearn.org/doc_index/#api). ### Tutorials - [TFLearn Quickstart](https://github.com/tflearn/tflearn/blob/master/tutorials/intro/quickstart.md). Learn the basics of TFLearn through a concrete machine learning task. Build and train a deep neural network classifier. ### Examples - [TFLearn Examples](https://github.com/tflearn/tflearn/blob/master/examples). A large collection of examples using TFLearn.

ML Frameworks

43.7K Github Stars

Open Source

TopDeepLearning

# Top Deep Learning Projects A list of popular github projects related to deep learning (ranked by stars). Last Update: 2020.07.09 | Project Name | Stars | Description | | ------- | ------ | ------ | |[tensorflow](https://github.com/tensorflow/tensorflow)|146k|An Open Source Machine Learning Framework for Everyone| |[keras](https://github.com/keras-team/keras)|48.9k|Deep Learning for humans| |[opencv](https://github.com/opencv/opencv)|46.1k|Open Source Computer Vision Library| |[pytorch](https://github.com/pytorch/pytorch)|40k|Tensors and Dynamic neural networks in Python with strong GPU acceleration| |[TensorFlow-Examples](https://github.com/aymericdamien/TensorFlow-Examples)|38.1k|TensorFlow Tutorial and Examples for Beginners (support TF v1 & v2)| |[tesseract](https://github.com/tesseract-ocr/tesseract)|35.3k|Tesseract Open Source OCR Engine (main repository)| |[face_recognition](https://github.com/ageitgey/face_recognition)|35.2k|The world's simplest facial recognition api for Python and the command line| |[faceswap](https://github.com/deepfakes/faceswap)|31.4k|Deepfakes Software For All| |[transformers](https://github.com/huggingface/transformers)|30.4k|🤗Transformers: State-of-the-art Natural Language Processing for Pytorch and TensorFlow 2.0.| |[100-Days-Of-ML-Code](https://github.com/Avik-Jain/100-Days-Of-ML-Code)|29.1k|100 Days of ML Coding| |[julia](https://github.com/JuliaLang/julia)|28.1k|The Julia Language: A fresh approach to technical computing.| |[gold-miner](https://github.com/xitu/gold-miner)|26.6k|🥇掘金翻译计划，可能是世界最大最好的英译中技术社区，最懂读者和译者的翻译平台：| |[awesome-scalability](https://github.com/binhnguyennus/awesome-scalability)|26.6k|The Patterns of Scalable, Reliable, and Performant Large-Scale Systems| |[basics](https://github.com/madewithml/basics)|24.5k|📚 Learn ML with clean code, simplified math and illustrative visuals.| |[bert](https://github.com/google-research/bert)|23.9k|TensorFlow code and pre-trained models for BERT| |[funNLP](https://github.com/fighting41love/funNLP)|22.1k|(Machine Learning)NLP面试中常考到的知识点和代码实现、nlp4han:中文自然语言处理工具集(断句/分词/词性标注/组块/句法分析/语义分析/NER/N元语法/HMM/代词消解/情感分析/拼写检查、XLM：Face…| |[xgboost](https://github.com/dmlc/xgboost)|19.4k|Scalable, Portable and Distributed Gradient Boosting (GBDT, GBRT or GBM) Library, for Python, R, Java, Scala, C++ and more. Runs on single machine, Hadoop, Spark, Flink and DataFlow| |[Real-Time-Voice-Cloning](https://github.com/CorentinJ/Real-Time-Voice-Cloning)|18.4k|Clone a voice in 5 seconds to generate arbitrary speech in real-time| |[d2l-zh](https://github.com/d2l-ai/d2l-zh)|17.9k|《动手学深度学习》：面向中文读者、能运行、可讨论。英文版即伯克利“深度学习导论”教材。| |[openpose](https://github.com/CMU-Perceptual-Computing-Lab/openpose)|17.8k|OpenPose: Real-time multi-person keypoint detection library for body, face, hands, and foot estimation| |[Coursera-ML-AndrewNg-Notes](https://github.com/fengdu78/Coursera-ML-AndrewNg-Notes)|17.7k|吴恩达老师的机器学习课程个人笔记| |[DeepFaceLab](https://github.com/iperov/DeepFaceLab)|17.3k|DeepFaceLab is the leading software for creating deepfakes.| |[pytorch-tutorial](https://github.com/yunjey/pytorch-tutorial)|17.3k|PyTorch Tutorial for Deep Learning Researchers| |[Mask_RCNN](https://github.com/matterport/Mask_RCNN)|17.2k|Mask R-CNN for object detection and instance segmentation on Keras and TensorFlow| |[spaCy](https://github.com/explosion/spaCy)|16.8k|💫 Industrial-strength Natural Language Processing (NLP) with Python and Cython| |[NLP-progress](https://github.com/sebastianruder/NLP-progress)|16.2k|Repository to track the progress in Natural Language Processing (NLP), including the datasets and the current state-of-the-art for the most common NLP tasks.| |[100-Days-Of-ML-Code](https://github.com/MLEveryday/100-Days-Of-ML-Code)|15.6k|100-Days-Of-ML-Code中文版| |[cs-video-courses](https://github.com/Developer-Y/cs-video-courses)|14.9k|List of Computer Science courses with video lectures.| |[WaveFunctionCollapse](https://github.com/mxgmn/WaveFunctionCollapse)|14.7k|Bitmap & tilemap generation from a single example with the help of ideas from quantum mechanics.| |[lectures](https://github.com/oxford-cs-deepnlp-2017/lectures)|14.7k|Oxford Deep NLP 2017 course| |[reinforcement-learning](https://github.com/dennybritz/reinforcement-learning)|14.7k|Implementation of Reinforcement Learning Algorithms. Python, OpenAI Gym, Tensorflow. Exercises and Solutions to accom…| |[pwc](https://github.com/zziz/pwc)|14.7k|Papers with code. Sorted by stars. Updated weekly.| |[TensorFlow-Course](https://github.com/machinelearningmindset/TensorFlow-Course)|14.6k|Simple and ready-to-use tutorials for TensorFlow| |[DeepSpeech](https://github.com/mozilla/DeepSpeech)|14.4k|A TensorFlow implementation of Baidu's DeepSpeech architecture| |[pumpkin-book](https://github.com/datawhalechina/pumpkin-book)|14k|《机器学习》（西瓜书）公式推导解析，在线阅读地址：https://datawhalechina.github.io/pumpkin-book| |[tfjs](https://github.com/tensorflow/tfjs)|13.5k|A WebGL accelerated JavaScript library for training and deploying ML models.| |[examples](https://github.com/pytorch/examples)|13.5k|A set of examples around pytorch in Vision, Text, Reinforcement Learning, etc.| |[openface](https://github.com/cmusatyalab/openface)|13.5k|Face recognition with deep neural networks.| |[Qix](https://github.com/ty4z2008/Qix)|13.3k|Machine Learning、Deep Learning、PostgreSQL、Distributed System、Node.Js、Golang| |[spleeter](https://github.com/deezer/spleeter)|12.7k|Deezer source separation library including pretrained models.| |[Virgilio](https://github.com/virgili0/Virgilio)|12.7k|Your new Mentor for Data Science E-Learning.| |[nndl.github.io](https://github.com/nndl/nndl.github.io)|12.7k|《神经网络与深度学习》邱锡鹏著 Neural Network and Deep Learning| |[Screenshot-to-code](https://github.com/emilwallner/Screenshot-to-code)|12.7k|A neural network that transforms a design mock-up into a static website.| |[pytorch-CycleGAN-and-pix2pix](https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix)|12.4k|Image-to-Image Translation in PyTorch| |[pytorch-handbook](https://github.com/zergtant/pytorch-handbook)|11.9k|pytorch handbook是一本开源的书籍，目标是帮助那些希望和使用PyTorch进行深度学习开发和研究的朋友快速入门，其中包含的Pytorch教程全部通过测试保证可以成功运行| |[gun](https://github.com/amark/gun)|11.9k|An open source cybersecurity protocol for syncing decentralized graph data.| |[Paddle](https://github.com/PaddlePaddle/Paddle)|11.8k|PArallel Distributed Deep LEarning: Machine Learning Framework from Industrial Practice （『飞桨』核心框架，深度学习&机器学习高性能单机、分布式训…| |[tensorflow-zh](https://github.com/jikexueyuanwiki/tensorflow-zh)|11.8k|谷歌全新开源人工智能系统TensorFlow官方文档中文版| |[darknet](https://github.com/AlexeyAB/darknet)|11.4k|YOLOv4 - Neural Networks for Object Detection (Windows and Linux version of Darknet )| |[learnopencv](https://github.com/spmallick/learnopencv)|11.4k|Learn OpenCV : C++ and Python Examples| |[neural-networks-and-deep-learning](https://github.com/mnielsen/neural-networks-and-deep-learning)|11.3k|Code samples for my book "Neural Networks and Deep Learning"| |[google-research](https://github.com/google-research/google-research)|11.2k|Google Research| |[labelImg](https://github.com/tzutalin/labelImg)|11.2k|🖍️ LabelImg is a graphical image annotation tool and label object bounding boxes in images| |[gensim](https://github.com/RaRe-Technologies/gensim)|11k|Topic Modelling for Humans| |[pix2code](https://github.com/tonybeltramelli/pix2code)|10.9k|pix2code: Generating Code from a Graphical User Interface Screenshot| |[facenet](https://github.com/davidsandberg/facenet)|10.8k|Face recognition using Tensorflow| |[DeOldify](https://github.com/jantic/DeOldify)|10.7k|A Deep Learning based project for colorizing and restoring old images (and video!)| |[python-machine-learning-book](https://github.com/rasbt/python-machine-learning-book)|10.7k|The "Python Machine Learning (1st edition)" book code repository and info resource| |[stanford-cs-229-machine-learning](https://github.com/afshinea/stanford-cs-229-machine-learning)|10.6k|VIP cheatsheets for Stanford's CS 229 Machine Learning| |[mmdetection](https://github.com/open-mmlab/mmdetection)|10.5k|OpenMMLab Detection Toolbox and Benchmark| |[face-api.js](https://github.com/justadudewhohacks/face-api.js)|10.4k|JavaScript API for face detection and face recognition in the browser and nodejs with tensorflow.js| |[Awesome-pytorch-list](https://github.com/bharathgs/Awesome-pytorch-list)|10.4k|A comprehensive list of pytorch related content on github,such as different models,implementations,helper libraries,t…| |[nsfw_data_scraper](https://github.com/alex000kim/nsfw_data_scraper)|10.2k|Collection of scripts to aggregate image data for the purposes of training an NSFW Image Classifier| |[convnetjs](https://github.com/karpathy/convnetjs)|10k|Deep Learning in Javascript. Train Convolutional Neural Networks (or ordinary ones) in your browser.| |[CycleGAN](https://github.com/junyanz/CycleGAN)|9.8k|Software that can generate photos from paintings, turn horses into zebras, perform style transfer, and more.| |[streamlit](https://github.com/streamlit/streamlit)|9.8k|Streamlit — The fastest way to build data apps in Python| |[DeepCreamPy](https://github.com/deeppomf/DeepCreamPy)|9.7k|Decensoring Hentai with Deep Neural Networks| |[stylegan](https://github.com/NVlabs/stylegan)|9.7k|StyleGAN - Official TensorFlow Implementation| |[Dive-into-DL-PyTorch](https://github.com/ShusenTang/Dive-into-DL-PyTorch)|9.6k|本项目将《动手学深度学习》(Dive into Deep Learning)原书中的MXNet实现改为PyTorch实现。| |[stanford-tensorflow-tutorials](https://github.com/chiphuyen/stanford-tensorflow-tutorials)|9.6k|This repository contains code examples for the Stanford's course: TensorFlow for Deep Learning Research.| |[horovod](https://github.com/horovod/horovod)|9.6k|Distributed training framework for TensorFlow, Keras, PyTorch, and Apache MXNet.| |[Deep-Learning-with-TensorFlow-book](https://github.com/dragen1860/Deep-Learning-with-TensorFlow-book)|9.4k|深度学习入门开源书，基于TensorFlow 2.0案例实战。Open source Deep Learning book, based on TensorFlow 2.0 framework.| |[neural-doodle](https://github.com/alexjc/neural-doodle)|9.4k|Turn your two-bit doodles into fine artworks with deep neural networks, generate seamless textures from photos, transfer style from one image to another, perform example-based upscaling, but wait... there's more! (An implementation of Semantic Style Transfer.)| |[caire](https://github.com/esimov/caire)|9.3k|Content aware image resize library| |[fast-style-transfer](https://github.com/lengstrom/fast-style-transfer)|9.2k|TensorFlow CNN for fast style transfer ⚡🖥🎨🖼| |[ncnn](https://github.com/Tencent/ncnn)|9.2k|ncnn is a high-performance neural network inference framework optimized for the mobile platform| |[kubeflow](https://github.com/kubeflow/kubeflow)|9.1k|Machine Learning Toolkit for Kubernetes| |[nltk](https://github.com/nltk/nltk)|9k|NLTK Source| |[flair](https://github.com/flairNLP/flair)|9k|A very simple framework for state-of-the-art Natural Language Processing (NLP)| |[ml-agents](https://github.com/Unity-Technologies/ml-agents)|9k|Unity Machine Learning Agents Toolkit| |[allennlp](https://github.com/allenai/allennlp)|8.8k|An open-source NLP research library, built on PyTorch.| |[botpress](https://github.com/botpress/botpress)|8.8k|🤖 The Conversational Platform with built-in language understanding (NLU), beautiful graphical interface and Dialog Manager (DM). Easily create chatbots and AI-based virtual assistants.| |[the-gan-zoo](https://github.com/hindupuravinash/the-gan-zoo)|8.7k|A list of all named GANs!| |[EffectiveTensorflow](https://github.com/vahidk/EffectiveTensorflow)|8.6k|TensorFlow tutorials and best practices.| |[tfjs-core](https://github.com/tensorflow/tfjs-core)|8.5k|WebGL-accelerated ML // linear algebra // automatic differentiation for JavaScript.| |[fairseq](https://github.com/pytorch/fairseq)|8.4k|Facebook AI Research Sequence-to-Sequence Toolkit written in Python.| |[sonnet](https://github.com/deepmind/sonnet)|8.4k|TensorFlow-based neural network library| |[mit-deep-learning-book-pdf](https://github.com/janishar/mit-deep-learning-book-pdf)|8.3k|MIT Deep Learning Book in PDF format (complete and parts) by Ian Goodfellow, Yoshua Bengio and Aaron Courville| |[TensorFlow-Tutorials](https://github.com/Hvass-Labs/TensorFlow-Tutorials)|8.3k|TensorFlow Tutorials with YouTube Videos| |[pytorch_geometric](https://github.com/rusty1s/pytorch_geometric)|8.2k|Geometric Deep Learning Extension Library for PyTorch| |[tutorials](https://github.com/MorvanZhou/tutorials)|8.2k|机器学习相关教程| |[fashion-mnist](https://github.com/zalandoresearch/fashion-mnist)|8k|A MNIST-like fashion product database. Benchmark 👉| |[bert-as-service](https://github.com/hanxiao/bert-as-service)|7.9k|Mapping a variable-length sentence to a fixed-length vector using BERT model| |[pix2pix](https://github.com/phillipi/pix2pix)|7.8k|Image-to-image translation with conditional adversarial nets| |[mediapipe](https://github.com/google/mediapipe)|7.7k|MediaPipe is the simplest way for researchers and developers to build world-class ML solutions and applications for mobile, edge, cloud and the web.| |[recommenders](https://github.com/microsoft/recommenders)|7.7k|Best Practices on Recommendation Systems| |[mit-deep-learning](https://github.com/lexfridman/mit-deep-learning)|7.7k|Tutorials, assignments, and competitions for MIT Deep Learning related courses.| |[pytorch-book](https://github.com/chenyuntc/pytorch-book)|7.6k|PyTorch tutorials and fun projects including neural talk, neural style, poem writing, anime generation (《深度学习框架PyTorch：入门与实战》)| |[Winds](https://github.com/GetStream/Winds)|7.6k|A Beautiful Open Source RSS & Podcast App Powered by Getstream.io| |[vid2vid](https://github.com/NVIDIA/vid2vid)|7.4k|Pytorch implementation of our method for high-resolution (e.g. 2048x1024) photorealistic video-to-video translation.| |[Learn_Machine_Learning_in_3_Months](https://github.com/llSourcell/Learn_Machine_Learning_in_3_Months)|7.3k|This is the code for "Learn Machine Learning in 3 Months" by Siraj Raval on Youtube| |[golearn](https://github.com/sjwhitworth/golearn)|7.3k|Machine Learning for Go| |[Keras-GAN](https://github.com/eriklindernoren/Keras-GAN)|7.2k|Keras implementations of Generative Adversarial Networks.| |[mlcourse.ai](https://github.com/Yorko/mlcourse.ai)|7k|Open Machine Learning Course| |[faceai](https://github.com/vipstone/faceai)|7k|一款入门级的人脸、视频、文字检测以及识别的项目.| |[pysc2](https://github.com/deepmind/pysc2)|6.9k|StarCraft II Learning Environment| |[pretrained-models.pytorch](https://github.com/Cadene/pretrained-models.pytorch)|6.9k|Pretrained ConvNets for pytorch: NASNet, ResNeXt, ResNet, InceptionV4, InceptionResnetV2, Xception, DPN, etc.| |[PyTorch-GAN](https://github.com/eriklindernoren/PyTorch-GAN)|6.7k|PyTorch implementations of Generative Adversarial Networks.| |[vision](https://github.com/pytorch/vision)|6.7k|Datasets, Transforms and Models specific to Computer Vision| |[nlp-tutorial](https://github.com/graykode/nlp-tutorial)|6.6k|Natural Language Processing Tutorial for Deep Learning Researchers| |[bullet3](https://github.com/bulletphysics/bullet3)|6.6k|Bullet Physics SDK: real-time collision detection and multi-physics simulation for VR, games, visual effects, robotics,| |[DCGAN-tensorflow](https://github.com/carpedm20/DCGAN-tensorflow)|6.6k|A tensorflow implementation of "Deep Convolutional Generative Adversarial Networks"| |[tfjs-models](https://github.com/tensorflow/tfjs-models)|6.5k|Pretrained models for TensorFlow.js| |[abu](https://github.com/bbfamily/abu)|6.5k|阿布量化交易系统(股票，期权，期货，比特币，机器学习) 基于python的开源量化交易，量化投资架构| |[pytorch-lightning](https://github.com/PyTorchLightning/pytorch-lightning)|6.5k|The lightweight PyTorch wrapper for ML researchers. Scale your models. Write less boilerplate| |[tensorboardX](https://github.com/lanpa/tensorboardX)|6.4k|tensorboard for pytorch (and chainer, mxnet, numpy, ...)| |[machine-learning-course](https://github.com/machinelearningmindset/machine-learning-course)|6.4k|💬 Machine Learning Course with Python:| |[guess](https://github.com/guess-js/guess)|6.3k|🔮 Libraries & tools for enabling Machine Learning driven user-experiences on the web| |[pyro](https://github.com/pyro-ppl/pyro)|6.3k|Deep universal probabilistic programming with Python and PyTorch| |[lab](https://github.com/deepmind/lab)|6.2k|A customisable 3D platform for agent-based AI research| |[mml-book.github.io](https://github.com/mml-book/mml-book.github.io)|6.2k|Companion webpage to the book "Mathematics For Machine Learning"| |[Interview](https://github.com/apachecn/Interview)|6.2k|Interview = 简历指南 + LeetCode + Kaggle| |[tensorlayer](https://github.com/tensorlayer/tensorlayer)|6.2k|Deep Learning and Reinforcement Learning Library for Scientists and Engineers 🔥| |[generative-models](https://github.com/wiseodd/generative-models)|6.1k|Collection of generative models, e.g. GAN, VAE in Pytorch and Tensorflow.| |[machine-learning-yearning-cn](https://github.com/deeplearning-ai/machine-learning-yearning-cn)|6.1k|Machine Learning Yearning 中文版 - 《机器学习训练秘籍》 - Andrew Ng 著| |[keras-yolo3](https://github.com/qqwweee/keras-yolo3)|6k|A Keras implementation of YOLOv3 (Tensorflow backend)| |[BossSensor](https://github.com/Hironsan/BossSensor)|5.9k|Hide screen when boss is approaching.| |[tensorflow2_tutorials_chinese](https://github.com/czy36mengfei/tensorflow2_tutorials_chinese)|5.9k|tensorflow2中文教程，持续更新(当前版本:tensorflow2.0)，tag: tensorflow 2.0 tutorials| |[TensorFlow-Tutorials](https://github.com/nlintz/TensorFlow-Tutorials)|5.9k|Simple tutorials using Google's TensorFlow Framework| |[argo](https://github.com/argoproj/argo)|5.9k|Argo Workflows: Get stuff done with Kubernetes.| |[python-machine-learning-book-2nd-edition](https://github.com/rasbt/python-machine-learning-book-2nd-edition)|5.8k|The "Python Machine Learning (2nd edition)" book code repository and info resource| |[dvc](https://github.com/iterative/dvc)|5.7k|🦉Data Version Control | Git for Data & Models| |[EasyPR](https://github.com/liuruoze/EasyPR)|5.7k|An easy, flexible, and accurate plate recognition project for Chinese licenses in unconstrained situations.| |[AdversarialNetsPapers](https://github.com/zhangqianhui/AdversarialNetsPapers)|5.6k|The classical paper list with code about generative adversarial nets| |[tensorpack](https://github.com/tensorpack/tensorpack)|5.6k|A Neural Net Training Interface on TensorFlow, with focus on speed + flexibility| |[photoprism](https://github.com/photoprism/photoprism)|5.6k|Personal Photo Management powered by Go and Google TensorFlow| |[tensorflow_cookbook](https://github.com/nfmcclure/tensorflow_cookbook)|5.6k|Code for Tensorflow Machine Learning Cookbook| |[albumentations](https://github.com/albumentations-team/albumentations)|5.6k|fast image augmentation library and easy to use wrapper around other libraries| |[swift](https://github.com/tensorflow/swift)|5.6k|Swift for TensorFlow| |[darkflow](https://github.com/thtrieu/darkflow)|5.6k|Translate darknet to tensorflow. 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Productivity ML Frameworks

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