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项目作者： tyami

项目描述 ：
Implementation of deep learning models
高级语言： Jupyter Notebook
项目地址: git://github.com/tyami/implementation-deep-learning.git
创建时间： 2018-03-14T08:49:17Z
项目社区：https://github.com/tyami/implementation-deep-learning
开源协议：
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Implementation of Deep learning model

Study materials

Hierarchy of deep learning milestones

1 Pytorch basics

  • Python basics
  • Pytorch basics
  • matplotlib

2 Neural Network (NN)

  • Perceptron
    • Activation functions (sigmoid, softmax, & ReLU)
  • Multi-Layer Perceptron
    • Backpropagation
  • Deep Neural Network

3 Basic concepts for NN modeling

  • Data Segmentation: Train/Validation/Test
  • Overfitting & Underfitting
    • Weight decay (Regularization)
    • Dropout
    • Input data transform
    • Learning rate decay
  • Convergence
    • Initialization
    • Batch Normalization
  • Optimization algorithm (Momentum, Nasterov, SGD, & Adam)

4 Datasets

  • Dataset: MNIST (10 classes, 28x28x1 handwriting images)
  • Dataset: ILSVRC (1,000 classes, 224x224x3 object images)

5 Convolutional Neural Network (CNN) series

  • Naive CNN
    • Convolution
    • Pooling
  • AlexNet
  • ZFNet
  • VGGNet
  • GoogLeNet
    • Inception module
    • Network In Network
  • ResNet
    • ResNet module
    • Bottleneck Architecture
  • DenseNet
  • ShuffleNet
    • Channel shuffle
    • Depthwise Seperable Convolution
  • Xception
  • MobileNet
  • MobileNetV2

6 Recurrent Neural Network (RNN) series

  • Naive RNN
  • Long Short-Term Memory (LSTM)
  • GRU
  • Dynamic RNN
  • Bidirectional RNN

7 Recurrent Convolutional Neural Network (RCNN) series

8 Autoencoder series

  • Restricted Boltzman Machine (RBM)
  • Deep Beilief Network (DBN)
  • Convolutional Autoencoder (CAE, CNN + Autoencoder)
  • Denoising Convolutional Autoencoder
  • Variational Autoencoder (VAE)

9 GAN