diff --git a/README.md b/README.md index e1c723e6..72cd998b 100644 --- a/README.md +++ b/README.md @@ -12,62 +12,96 @@ It is suitable for beginners who want to find clear and concise examples about T #### 0 - Prerequisite - [Introduction to Machine Learning](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/0_Prerequisite/ml_introduction.ipynb). + [](https://beta.deepnote.com/launch?template=data-science&url=https%3A%2F%2Fgithub.com%2Faymericdamien%2FTensorFlow-Examples%2Fblob%2Fmaster%2Fnotebooks%2F0_Prerequisite%2Fml_introduction.ipynb) - [Introduction to MNIST Dataset](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/0_Prerequisite/mnist_dataset_intro.ipynb). + [](https://beta.deepnote.com/launch?template=data-science&url=https%3A%2F%2Fgithub.com%2Faymericdamien%2FTensorFlow-Examples%2Fblob%2Fmaster%2Fnotebooks%2F0_Prerequisite%2Fmnist_dataset_intro.ipynb) #### 1 - Introduction -- **Hello World** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/1_Introduction/helloworld.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/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/1_Introduction/basic_operations.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/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/notebooks/1_Introduction/basic_eager_api.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/1_Introduction/basic_eager_api.py)). Get started with TensorFlow's Eager API. +- **Hello World** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/1_Introduction/helloworld.ipynb)) [](https://beta.deepnote.com/launch?template=data-science&url=https%3A%2F%2Fgithub.com%2Faymericdamien%2FTensorFlow-Examples%2Fblob%2Fmaster%2Fnotebooks%2F1_Introduction%2Fhelloworld.ipynb) + ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/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/1_Introduction/basic_operations.ipynb)) [](https://beta.deepnote.com/launch?template=data-science&url=https%3A%2F%2Fgithub.com%2Faymericdamien%2FTensorFlow-Examples%2Fblob%2Fmaster%2Fnotebooks%2F1_Introduction%2Fbasic_operations.ipynb) + ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/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/notebooks/1_Introduction/basic_eager_api.ipynb)) [](https://beta.deepnote.com/launch?template=data-science&url=https%3A%2F%2Fgithub.com%2Faymericdamien%2FTensorFlow-Examples%2Fblob%2Fmaster%2Fnotebooks%2F1_Introduction%2Fbasic_eager_api.ipynb) + ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/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/notebooks/2_BasicModels/linear_regression.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/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/notebooks/2_BasicModels/linear_regression_eager_api.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/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/notebooks/2_BasicModels/logistic_regression.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/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/notebooks/2_BasicModels/logistic_regression_eager_api.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/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/notebooks/2_BasicModels/nearest_neighbor.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/2_BasicModels/nearest_neighbor.py)). Implement Nearest Neighbor algorithm with TensorFlow. -- **K-Means** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/2_BasicModels/kmeans.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/2_BasicModels/kmeans.py)). Build a K-Means classifier with TensorFlow. -- **Random Forest** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/2_BasicModels/random_forest.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/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/notebooks/2_BasicModels/gradient_boosted_decision_tree.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/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/notebooks/2_BasicModels/word2vec.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/2_BasicModels/word2vec.py)). Build a Word Embedding Model (Word2Vec) from Wikipedia data, with TensorFlow. +- **Linear Regression** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/2_BasicModels/linear_regression.ipynb)) [](https://beta.deepnote.com/launch?template=data-science&url=https%3A%2F%2Fgithub.com%2Faymericdamien%2FTensorFlow-Examples%2Fblob%2Fmaster%2Fnotebooks%2F2_BasicModels%2Flinear_regression.ipynb) + ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/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/notebooks/2_BasicModels/linear_regression_eager_api.ipynb)) [](https://beta.deepnote.com/launch?template=data-science&url=https%3A%2F%2Fgithub.com%2Faymericdamien%2FTensorFlow-Examples%2Fblob%2Fmaster%2Fnotebooks%2F2_BasicModels%2Flinear_regression_eager_api.ipynb) + ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/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/notebooks/2_BasicModels/logistic_regression.ipynb)) [](https://beta.deepnote.com/launch?template=data-science&url=https%3A%2F%2Fgithub.com%2Faymericdamien%2FTensorFlow-Examples%2Fblob%2Fmaster%2Fnotebooks%2F2_BasicModels%2Flogistic_regression.ipynb) + ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/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/notebooks/2_BasicModels/logistic_regression_eager_api.ipynb)) [](https://beta.deepnote.com/launch?template=data-science&url=https%3A%2F%2Fgithub.com%2Faymericdamien%2FTensorFlow-Examples%2Fblob%2Fmaster%2Fnotebooks%2F2_BasicModels%2Flogistic_regression_eager_api.ipynb) + ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/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/notebooks/2_BasicModels/nearest_neighbor.ipynb)) [](https://beta.deepnote.com/launch?template=data-science&url=https%3A%2F%2Fgithub.com%2Faymericdamien%2FTensorFlow-Examples%2Fblob%2Fmaster%2Fnotebooks%2F2_BasicModels%2Fnearest_neighbor.ipynb) + ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/2_BasicModels/nearest_neighbor.py)). Implement Nearest Neighbor algorithm with TensorFlow. +- **K-Means** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/2_BasicModels/kmeans.ipynb)) [](https://beta.deepnote.com/launch?template=data-science&url=https%3A%2F%2Fgithub.com%2Faymericdamien%2FTensorFlow-Examples%2Fblob%2Fmaster%2Fnotebooks%2F2_BasicModels%2Fkmeans.ipynb) + ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/2_BasicModels/kmeans.py)). Build a K-Means classifier with TensorFlow. +- **Random Forest** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/2_BasicModels/random_forest.ipynb)) [](https://beta.deepnote.com/launch?template=data-science&url=https%3A%2F%2Fgithub.com%2Faymericdamien%2FTensorFlow-Examples%2Fblob%2Fmaster%2Fnotebooks%2F2_BasicModels%2Frandom_forest.ipynb) + ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/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/notebooks/2_BasicModels/gradient_boosted_decision_tree.ipynb)) [](https://beta.deepnote.com/launch?template=data-science&url=https%3A%2F%2Fgithub.com%2Faymericdamien%2FTensorFlow-Examples%2Fblob%2Fmaster%2Fnotebooks%2F2_BasicModels%2Fgradient_boosted_decision_tree.ipynb) + ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/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/notebooks/2_BasicModels/word2vec.ipynb)) [](https://beta.deepnote.com/launch?template=data-science&url=https%3A%2F%2Fgithub.com%2Faymericdamien%2FTensorFlow-Examples%2Fblob%2Fmaster%2Fnotebooks%2F2_BasicModels%2Fword2vec.ipynb) + ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/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/notebooks/3_NeuralNetworks/neural_network_raw.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/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/notebooks/3_NeuralNetworks/neural_network.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/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/notebooks/3_NeuralNetworks/neural_network_eager_api.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/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/notebooks/3_NeuralNetworks/convolutional_network_raw.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/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/notebooks/3_NeuralNetworks/convolutional_network.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/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/notebooks/3_NeuralNetworks/recurrent_network.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/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/notebooks/3_NeuralNetworks/bidirectional_rnn.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/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/notebooks/3_NeuralNetworks/dynamic_rnn.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/3_NeuralNetworks/dynamic_rnn.py)). Build a recurrent neural network (LSTM) that performs dynamic calculation to classify sequences of different length. +- **Simple Neural Network** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/3_NeuralNetworks/neural_network_raw.ipynb)) [](https://beta.deepnote.com/launch?template=data-science&url=https%3A%2F%2Fgithub.com%2Faymericdamien%2FTensorFlow-Examples%2Fblob%2Fmaster%2Fnotebooks%2F3_NeuralNetworks%2Fneural_network_raw.ipynb) + ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/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/notebooks/3_NeuralNetworks/neural_network.ipynb)) [](https://beta.deepnote.com/launch?template=data-science&url=https%3A%2F%2Fgithub.com%2Faymericdamien%2FTensorFlow-Examples%2Fblob%2Fmaster%2Fnotebooks%2F3_NeuralNetworks%2Fneural_network.ipynb) + ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/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/notebooks/3_NeuralNetworks/neural_network_eager_api.ipynb)) [](https://beta.deepnote.com/launch?template=data-science&url=https%3A%2F%2Fgithub.com%2Faymericdamien%2FTensorFlow-Examples%2Fblob%2Fmaster%2Fnotebooks%2F3_NeuralNetworks%2Fneural_network_eager_api.ipynb) + ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/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/notebooks/3_NeuralNetworks/convolutional_network_raw.ipynb)) [](https://beta.deepnote.com/launch?template=data-science&url=https%3A%2F%2Fgithub.com%2Faymericdamien%2FTensorFlow-Examples%2Fblob%2Fmaster%2Fnotebooks%2F3_NeuralNetworks%2Fconvolutional_network_raw.ipynb) + ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/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/notebooks/3_NeuralNetworks/convolutional_network.ipynb)) [](https://beta.deepnote.com/launch?template=data-science&url=https%3A%2F%2Fgithub.com%2Faymericdamien%2FTensorFlow-Examples%2Fblob%2Fmaster%2Fnotebooks%2F3_NeuralNetworks%2Fconvolutional_network.ipynb) + ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/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/notebooks/3_NeuralNetworks/recurrent_network.ipynb)) [](https://beta.deepnote.com/launch?template=data-science&url=https%3A%2F%2Fgithub.com%2Faymericdamien%2FTensorFlow-Examples%2Fblob%2Fmaster%2Fnotebooks%2F3_NeuralNetworks%2Frecurrent_network.ipynb) + ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/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/notebooks/3_NeuralNetworks/bidirectional_rnn.ipynb)) [](https://beta.deepnote.com/launch?template=data-science&url=https%3A%2F%2Fgithub.com%2Faymericdamien%2FTensorFlow-Examples%2Fblob%2Fmaster%2Fnotebooks%2F3_NeuralNetworks%2Fbidirectional_rnn.ipynb) + ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/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/notebooks/3_NeuralNetworks/dynamic_rnn.ipynb)) [](https://beta.deepnote.com/launch?template=data-science&url=https%3A%2F%2Fgithub.com%2Faymericdamien%2FTensorFlow-Examples%2Fblob%2Fmaster%2Fnotebooks%2F3_NeuralNetworks%2Fdynamic_rnn.ipynb) + ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/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/notebooks/3_NeuralNetworks/autoencoder.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/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/notebooks/3_NeuralNetworks/variational_autoencoder.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/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/notebooks/3_NeuralNetworks/gan.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/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/notebooks/3_NeuralNetworks/dcgan.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/3_NeuralNetworks/dcgan.py)). Build a Deep Convolutional Generative Adversarial Network (DCGAN) to generate images from noise. +- **Auto-Encoder** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/3_NeuralNetworks/autoencoder.ipynb)) [](https://beta.deepnote.com/launch?template=data-science&url=https%3A%2F%2Fgithub.com%2Faymericdamien%2FTensorFlow-Examples%2Fblob%2Fmaster%2Fnotebooks%2F3_NeuralNetworks%2Fautoencoder.ipynb) + ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/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/notebooks/3_NeuralNetworks/variational_autoencoder.ipynb)) [](https://beta.deepnote.com/launch?template=data-science&url=https%3A%2F%2Fgithub.com%2Faymericdamien%2FTensorFlow-Examples%2Fblob%2Fmaster%2Fnotebooks%2F3_NeuralNetworks%2Fvariational_autoencoder.ipynb) + ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/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/notebooks/3_NeuralNetworks/gan.ipynb)) [](https://beta.deepnote.com/launch?template=data-science&url=https%3A%2F%2Fgithub.com%2Faymericdamien%2FTensorFlow-Examples%2Fblob%2Fmaster%2Fnotebooks%2F3_NeuralNetworks%2Fgan.ipynb) + ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/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/notebooks/3_NeuralNetworks/dcgan.ipynb)) [](https://beta.deepnote.com/launch?template=data-science&url=https%3A%2F%2Fgithub.com%2Faymericdamien%2FTensorFlow-Examples%2Fblob%2Fmaster%2Fnotebooks%2F3_NeuralNetworks%2Fdcgan.ipynb) + ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/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/notebooks/4_Utils/save_restore_model.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/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/notebooks/4_Utils/tensorboard_basic.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/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/notebooks/4_Utils/tensorboard_advanced.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/4_Utils/tensorboard_advanced.py)). Going deeper into Tensorboard; visualize the variables, gradients, and more... +- **Save and Restore a model** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/4_Utils/save_restore_model.ipynb)) [](https://beta.deepnote.com/launch?template=data-science&url=https%3A%2F%2Fgithub.com%2Faymericdamien%2FTensorFlow-Examples%2Fblob%2Fmaster%2Fnotebooks%2F4_Utils%2Fsave_restore_model.ipynb) + ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/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/notebooks/4_Utils/tensorboard_basic.ipynb)) [](https://beta.deepnote.com/launch?template=data-science&url=https%3A%2F%2Fgithub.com%2Faymericdamien%2FTensorFlow-Examples%2Fblob%2Fmaster%2Fnotebooks%2F4_Utils%2Ftensorboard_basic.ipynb) + ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/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/notebooks/4_Utils/tensorboard_advanced.ipynb)) [](https://beta.deepnote.com/launch?template=data-science&url=https%3A%2F%2Fgithub.com%2Faymericdamien%2FTensorFlow-Examples%2Fblob%2Fmaster%2Fnotebooks%2F4_Utils%2Ftensorboard_advanced.ipynb) + ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/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/notebooks/5_DataManagement/build_an_image_dataset.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/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/notebooks/5_DataManagement/tensorflow_dataset_api.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/5_DataManagement/tensorflow_dataset_api.py)). Introducing TensorFlow Dataset API for optimizing the input data pipeline. +- **Build an image dataset** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/5_DataManagement/build_an_image_dataset.ipynb)) [](https://beta.deepnote.com/launch?template=data-science&url=https%3A%2F%2Fgithub.com%2Faymericdamien%2FTensorFlow-Examples%2Fblob%2Fmaster%2Fnotebooks%2F5_DataManagement%2Fbuild_an_image_dataset.ipynb) + ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/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/notebooks/5_DataManagement/tensorflow_dataset_api.ipynb)) [](https://beta.deepnote.com/launch?template=data-science&url=https%3A%2F%2Fgithub.com%2Faymericdamien%2FTensorFlow-Examples%2Fblob%2Fmaster%2Fnotebooks%2F5_DataManagement%2Ftensorflow_dataset_api.ipynb) + ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/5_DataManagement/tensorflow_dataset_api.py)). Introducing TensorFlow Dataset API for optimizing the input data pipeline. #### 6 - Multi GPU -- **Basic Operations on multi-GPU** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/6_MultiGPU/multigpu_basics.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/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/notebooks/6_MultiGPU/multigpu_cnn.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/6_MultiGPU/multigpu_cnn.py)). A clear and simple TensorFlow implementation to train a convolutional neural network on multiple GPUs. +- **Basic Operations on multi-GPU** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/6_MultiGPU/multigpu_basics.ipynb)) + ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/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/notebooks/6_MultiGPU/multigpu_cnn.ipynb)) + ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/6_MultiGPU/multigpu_cnn.py)). A clear and simple TensorFlow implementation to train a convolutional neural network on multiple GPUs. ## TensorFlow 2.0 The tutorial index for TF v2 is available here: [TensorFlow 2.0 Examples](tensorflow_v2). ## Dataset + [](https://beta.deepnote.com/launch?template=data-science&url=https%3A%2F%2Fgithub.com%2Faymericdamien%2FTensorFlow-Examples%2Fblob%2Fmaster%2Fnotebooks%2F0_Prerequisite%2Fmnist_dataset_intro.ipynb) 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). +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/).