Tutorial index
0 - Prerequisite
1 - Introduction
- Hello World (notebook) (code). Very simple example to learn how to print "hello world" using TensorFlow.
- Basic Operations (notebook) (code). A simple example that cover TensorFlow basic operations.
- TensorFlow Eager API basics (notebook) (code). Get started with TensorFlow's Eager API.
2 - Basic Models
- Linear Regression (notebook) (code). Implement a Linear Regression with TensorFlow.
- Linear Regression (eager api) (notebook) (code). Implement a Linear Regression using TensorFlow's Eager API.
- Logistic Regression (notebook) (code). Implement a Logistic Regression with TensorFlow.
- Logistic Regression (eager api) (notebook) (code). Implement a Logistic Regression using TensorFlow's Eager API.
- Nearest Neighbor (notebook) (code). Implement Nearest Neighbor algorithm with TensorFlow.
- K-Means (notebook) (code). Build a K-Means classifier with TensorFlow.
- Random Forest (notebook) (code). Build a Random Forest classifier with TensorFlow.
- Gradient Boosted Decision Tree (GBDT) (notebook) (code). Build a Gradient Boosted Decision Tree (GBDT) with TensorFlow.
- Word2Vec (Word Embedding) (notebook) (code). Build a Word Embedding Model (Word2Vec) from Wikipedia data, with TensorFlow.
3 - Neural Networks
Supervised
- Simple Neural Network (notebook) (code). 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) (code). 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) (code). 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) (code). Build a convolutional neural network to classify MNIST digits dataset. Raw TensorFlow implementation.
- Convolutional Neural Network (tf.layers/estimator api) (notebook) (code). Use TensorFlow 'layers' and 'estimator' API to build a convolutional neural network to classify MNIST digits dataset.
- Recurrent Neural Network (LSTM) (notebook) (code). Build a recurrent neural network (LSTM) to classify MNIST digits dataset.
- Bi-directional Recurrent Neural Network (LSTM) (notebook) (code). Build a bi-directional recurrent neural network (LSTM) to classify MNIST digits dataset.
- Dynamic Recurrent Neural Network (LSTM) (notebook) (code). Build a recurrent neural network (LSTM) that performs dynamic calculation to classify sequences of different length.
Unsupervised
- Auto-Encoder (notebook) (code). Build an auto-encoder to encode an image to a lower dimension and re-construct it.
- Variational Auto-Encoder (notebook) (code). Build a variational auto-encoder (VAE), to encode and generate images from noise.
- GAN (Generative Adversarial Networks) (notebook) (code). Build a Generative Adversarial Network (GAN) to generate images from noise.
- DCGAN (Deep Convolutional Generative Adversarial Networks) (notebook) (code). Build a Deep Convolutional Generative Adversarial Network (DCGAN) to generate images from noise.
4 - Utilities
- Save and Restore a model (notebook) (code). Save and Restore a model with TensorFlow.
- Tensorboard - Graph and loss visualization (notebook) (code). Use Tensorboard to visualize the computation Graph and plot the loss.
- Tensorboard - Advanced visualization (notebook) (code). Going deeper into Tensorboard; visualize the variables, gradients, and more...
5 - Data Management
- Build an image dataset (notebook) (code). Build your own images dataset with TensorFlow data queues, from image folders or a dataset file.
- TensorFlow Dataset API (notebook) (code). Introducing TensorFlow Dataset API for optimizing the input data pipeline.
6 - Multi GPU
- Basic Operations on multi-GPU (notebook) (code). A simple example to introduce multi-GPU in TensorFlow.
- Train a Neural Network on multi-GPU (notebook) (code). A clear and simple TensorFlow implementation to train a convolutional neural network on multiple GPUs.
https://github.com/aymericdamien/TensorFlow-Examples