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Created by YunYang1994

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. 🔥🔥🔥
  • AutoEncoder (notebook) (code)(paper). AutoEncoder: Reducing the Dimensionality of Data with Neural Networks.

4 - Object Detection

  • MTCNN (notebook) (code)(paper). MTCNN: Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Networks. (Face detection and Alignment) 🔥🔥

  • YOLOv3 (notebook) (code)(paper). YOLOv3: An Incremental Improvement.🔥🔥🔥🔥

  • SSD (notebook) (code)(paper). SSD: Single Shot MultiBox Detector.🔥🔥🔥🔥 【TO DO】

  • Faster R-CNN (notebook) (code)(paper). Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks.🔥🔥🔥🔥 【TO DO】

5 - Image Segmentation

  • FCN (notebook) (code)(paper). FCN: Fully Convolutional Networks for Semantic Segmentation. 🔥🔥🔥🔥🔥

  • Unet (notebook) (code)(paper). U-Net: Convolutional Networks for Biomedical Image 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 - Utils

  • Multiple GPU Training (notebook)(code). Use multiple GPU to train your model.
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