https://github.com/hellocybernetics/TensorFlow2.0_Eager_Execution_Tutorials

Tutorials of TensorFlow eager execution
https://github.com/hellocybernetics/TensorFlow2.0_Eager_Execution_Tutorials

deep-learning eager-execution machine-learning tensorflow tutorial

Last synced: about 2 months ago
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Tutorials of TensorFlow eager execution

• Host: GitHub
• URL: https://github.com/hellocybernetics/TensorFlow2.0_Eager_Execution_Tutorials
• Owner: hellocybernetics
• Created: 2018-12-01T03:44:32.000Z (about 6 years ago)
• Default Branch: master
• Last Pushed: 2019-12-01T10:36:46.000Z (about 5 years ago)
• Last Synced: 2024-08-01T03:14:36.764Z (4 months ago)
• Topics: deep-learning, eager-execution, machine-learning, tensorflow, tutorial
• Language: Jupyter Notebook
• Homepage:
• Size: 17.5 MB
• Stars: 30
• Watchers: 2
• Forks: 13
• Open Issues: 1
• Metadata Files:
  • Readme: README.md

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• awesome-tensorflow-2 - TensorFlow2.0_Eager_Execution_Tutorials

README

        
# TensorFlow2.0_Eager_Execution_Tutorials

This repository provides tutorial code of TensorFlow2.0 . This tutorials refer to the PyTorch tutorials 

https://github.com/yunjey/pytorch-tutorial

## Table of Contents

### 0. Low Level

* [Using_tf.Variable](https://github.com/hellocybernetics/TensorFlow_Eager_Execution_Tutorials/blob/master/tutorials/00_lowlevel/LinearModel_using_tf_Variable.ipynb)

* [TF_Eager Basics](https://github.com/hellocybernetics/TensorFlow_Eager_Execution_Tutorials/blob/master/tutorials/01_basics/TF_eager_basics.ipynb) 

* [eager_vs_graph](https://github.com/hellocybernetics/TensorFlow_Eager_Execution_Tutorials/blob/master/tutorials/00_lowlevel/eager_vs_graph.ipynb)

### 1. Basics

* [KerasAPI Sequential_and_Model](https://github.com/hellocybernetics/TensorFlow_Eager_Execution_Tutorials/blob/master/tutorials/01_basics/Sequential_and_Model.ipynb)

* [Linear Regression](https://github.com/hellocybernetics/TensorFlow_Eager_Execution_Tutorials/blob/master/tutorials/01_basics/linear_regression.ipynb)

* [Logistic Regression](https://github.com/hellocybernetics/TensorFlow_Eager_Execution_Tutorials/blob/master/tutorials/01_basics/logistic_regression.ipynb)

* [Feedforward Neural Network](https://github.com/hellocybernetics/TensorFlow_Eager_Execution_Tutorials/blob/master/tutorials/01_basics/feed_forward_neural_network.ipynb)

### 2. Intermediate

* [Convolutional Neural Network](https://github.com/hellocybernetics/TensorFlow_Eager_Execution_Tutorials/blob/master/tutorials/02_intermediate/Convolutinal_Neural_Network.ipynb)

* [Convolutional Neural Network using Keras API](https://github.com/hellocybernetics/TensorFlow_Eager_Execution_Tutorials/blob/master/tutorials/02_intermediate/Convolutinal_Neural_Network_using_KerasAPI.ipynb)

* [Deep Residual Network](https://github.com/hellocybernetics/TensorFlow_Eager_Execution_Tutorials/blob/master/tutorials/02_intermediate/residual_network.ipynb)

* [Deep Residual Network using Keras API](https://github.com/hellocybernetics/TensorFlow_Eager_Execution_Tutorials/blob/master/tutorials/02_interimediate/residual_network.ipynb)

* [Recurrent Neural Network using Keras API](https://github.com/hellocybernetics/TensorFlow_Eager_Execution_Tutorials/blob/master/tutorials/02_intermediate/Recurrent_Neural_Network.ipynb)

* [Bidirectional Recurrent Neural Network using Keras API](https://github.com/hellocybernetics/TensorFlow_Eager_Execution_Tutorials/blob/master/tutorials/02_intermediate/Bidrectional_Recurrent_Neural_Network.ipynb)

* Language Model (RNN-LM)

### 3. Advanced

* [Generative Adversarial Network](https://github.com/hellocybernetics/TensorFlow_Eager_Execution_Tutorials/blob/master/tutorials/03_advanced/Generative_Adversarial_Network.ipynb)

* [Variational Auto-Encoder](https://github.com/hellocybernetics/TensorFlow_Eager_Execution_Tutorials/blob/master/tutorials/03_advanced/Variational_Auto_Encoder.ipynb)

* [Graph Convolution](https://github.com/hellocybernetics/TensorFlow_Eager_Execution_Tutorials/blob/master/tutorials/03_advanced/graph_convnet.ipynb)

* Neural Style Transfer

* Image Captioning (CNN-RNN)

### 4. Probability

* [MCMC Regression with JointDistribution](https://github.com/hellocybernetics/TensorFlow2.0_Eager_Execution_Tutorials/blob/master/tutorials/04_probability/Regiression_with_tfd.JointDistributionCoroutine.ipynb)

* [Variational Regression with JointDistribution](https://github.com/hellocybernetics/TensorFlow2.0_Eager_Execution_Tutorials/blob/master/tutorials/04_probability/VariationalRegression_JointDistribution.ipynb)

* [basic modeling with tfp.layers](https://github.com/hellocybernetics/TensorFlow2.0_Eager_Execution_Tutorials/blob/master/tutorials/04_probability/probabilistic_model_by_tfp_layers.ipynb)

### X. Others

* [Hyper Parameter Optmization](https://github.com/hellocybernetics/TensorFlow_Eager_Execution_Tutorials/blob/master/tutorials/99_others/hyper_parameter_optimization_using_optuna.ipynb)

* [eager vs pytorch speed](https://github.com/hellocybernetics/TensorFlow_Eager_Execution_Tutorials/blob/master/tutorials/99_others/Eager_vs_PyTorch_speed.ipynb)

* [learning discontinuity](https://github.com/hellocybernetics/TensorFlow_Eager_Execution_Tutorials/blob/master/tutorials/99_others/learninig_discontinuity.ipynb)

* [dropout bayes neural network](https://github.com/hellocybernetics/TensorFlow_Eager_Execution_Tutorials/blob/master/tutorials/99_others/dropout_bayes.ipynb)

## Dependencies

My environment is Google Colab or

Python 3.7

TensorFlow 2.0

TensorFlow Probability 0.9.0 (nightly)