Deep learning tutorials

July 26, 2016 ยท View on GitHub

Deep learning tutorials (2nd ed.)

Week1

  1. Deep learning intro.
  2. Python basics
  3. Let's play with images & MNIST
  4. Terminologies

Week2 - Do you know deep learning?

  1. CNN and AlexNet
  2. TensorFlow basics
  3. Logistic regression
  4. GoogLeNet
  5. AlphaGo: MCTS+CNN
  6. Let's implement MLP!
  7. Let's play with you OWN DATASET
  8. Regularization methods
  9. Optimization methods
  10. Restricted Boltzmann Machine
  11. Let's implement denoising autoencoder

Week3 - CNN basics

  1. Semantic segmentation: FCN, DeconvNet, DeepLab with atrous conv
  2. Let's implement a simple CNN
  3. Let's implement a basic CNN
  4. Let's implement semantic segmentation
  5. Weakly supervised localization: Global average pooling
  6. Implement MLP and CNN on your OWN DATASET
  7. Denoising deconvolutional neural network

Week4 - CNN applications + RNN basics

  1. Image detection (RCNN, SPPnet, FastRCNN, FasterRCNN)
  2. Other detections (YOLO, AttentionNet)
  3. Let's use TensorBoards
  4. RNN from Colah's blog
  5. Visual QnA: DPPnet + MCBP!
  6. Super resolution
  7. Deep reinforcement learning

Week5 - RNN applications

  1. RNN basic + handwriting generation
  2. Let's implement RNNs
  3. Let's implement Word2vec
  4. Image captioning: Show and Tell + Show, attend and tell
  5. char-rnn + how can we use Hangul?

Week6 - Deep learning is so FUN!

  1. Residual network and some analysis
  2. Neural Style: Texture synsthesis+Inverting CNN
  3. Let's implement neural style
  4. Bayesian optimization
  5. Adversaral attack?
  6. Generative adversarial network