/TensorFlow2.0-Examples

💥 Tensorflow2.x Examples from basic to hard

Primary LanguageJupyter NotebookMIT LicenseMIT

Tensorflow2.0 Examples

"Talk is cheap, show me the code."
                                                                     --------- Linus Torvalds

This tutorial was designed for easily diving into TensorFlow2.0. it includes both notebooks and source codes with explanation. It will be continuously updated ! 🐍🐍🐍🐍🐍🐍

1 - Introduction

  • Hello World (notebook) (code). Very simple example to learn how to print "hello world" using TensorFlow.
  • Variable (notebook) (code). Learn to use variable in tensorflow.
  • Basical operation (notebook) (code). A simple example that covers TensorFlow basic operations.
  • Activation (notebook) (code). Start to know some activation functions in tensorflow.
  • GradientTape (notebook) (code). Introduce a key technique for automatic differentiation

2 - Basical Models

  • Linear Regression (notebook) (code). Implement a Linear Regression with TensorFlow.
  • Logistic Regression (notebook) (code). Implement a Logistic Regression with TensorFlow.
  • Multilayer Perceptron Layer (notebook) (code). Implement Multi-Layer Perceptron Model with TensorFlow.
  • CNN (notebook) (code). Implement CNN Model with TensorFlow.

3 - Neural Network Architecture

  • VGG16 (notebook) (code)(paper). VGG16: Very Deep Convolutional Networks for Large-Scale Image Recognition.
  • Resnet (notebook) (code)(paper). Resnet: Deep Residual Learning for Image Recognition.

4 - Object Detection

5 - Image Segmentation

  • Unet (notebook) (code)(paper). U-Net: Convolutional Networks for Biomedical Image Segmentation. 🔥🔥
  • FCN (notebook) (code)(paper). FCN: Fully Convolutional Networks for Semantic Segmentation. 🔥🔥🔥

6 - Generative Adversarial Networks

  • DCGAN (notebook) (code)(paper). Deep Convolutional Generative Adversarial Network.
  • Pix2Pix (notebook) (code)(paper). Image-to-Image Translation with Conditional Adversarial Networks.

7 - Reinforcement Learning