This tutorial was designed for easily diving into TensorFlow, through examples. For readability, it includes both notebooks and source codes with explanation.

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TensorFlow Examples

Code examples for some popular machine learning algorithms, using TensorFlow library. This tutorial is designed to easily dive into TensorFlow, through examples. It includes both notebook and code with explanations.

Tutorial index

1 - Introduction

Hello World (notebook) (code)
Basic Operations (notebook) (code)

2 - Basic Classifiers

Nearest Neighbor (notebook) (code)
Linear Regression (notebook) (code)
Logistic Regression (notebook) (code)

3 - Neural Networks

Multilayer Perceptron (notebook) (code)
Convolutional Neural Network (notebook) (code)
AlexNet (notebook) (code)
Reccurent Neural Network (LSTM) (notebook) (code)
Bidirectional Reccurent Neural Network (LSTM) (notebook) (code)

4 - Multi GPU

Basic Operations on multi-GPU (notebook) (code)

5 - User Interface (Tensorboard)

Graph Visualization (notebook) (code)
Loss Visualization (notebook) (code)

Dependencies

tensorflow
numpy
matplotlib
cuda (to run examples on GPU)

For more details about TensorFlow installation, you can check Setup_TensorFlow.md

Dataset

Some examples require MNIST dataset for training and testing. Don't worry, this dataset will automatically be downloaded when running examples (with input_data.py). MNIST is a database of handwritten digits, with 60,000 examples for training and 10,000 examples for testing. (Website: http://yann.lecun.com/exdb/mnist/)

Other tutorials are coming soon....

README.md