# dl_tutorials

**Repository Path**: hoody/dl_tutorials

## Basic Information

- **Project Name**: dl_tutorials
- **Description**: dl_tutorials

- **Primary Language**: Unknown
- **License**: MIT
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2016-07-25
- **Last Updated**: 2020-12-19

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# Deep learning tutorials
 Deep learning tutorials (2nd ed.)

## Week1 
1. [Deep learning intro.](https://github.com/sjchoi86/dl_tutorials/blob/master/presentations/Week1-1b%20Deep%20learning%20intro.pptx)
2. [Python basics](https://github.com/sjchoi86/dl_tutorials/blob/master/presentations/Week1-1c%20Python%20basic%20(basic_python).pptx)
3. [Let's play with images & MNIST](https://github.com/sjchoi86/dl_tutorials/blob/master/presentations/Week1-1d%20MNIST%20(basic_mnist)%20and%20image%20processing%20(basic_imgprocess).pptx)
4. [Terminologies](https://github.com/sjchoi86/dl_tutorials/blob/master/presentations/Week1-2a%20Terminologies.pptx)

## Week2 - Do you know deep learning?
1. [CNN and AlexNet](https://github.com/sjchoi86/dl_tutorials/blob/master/presentations/Week1-2b%20CNN%20and%20AlexNet.pptx)
2. [TensorFlow basics](https://github.com/sjchoi86/dl_tutorials/blob/master/presentations/Week1-2c%20TensorFlow%20basic%20(basic_tensorflow).pptx)
3. [Logistic regression](https://github.com/sjchoi86/dl_tutorials/blob/master/presentations/Week1-2d%20Logistic%20regression%20(logistic_regression_mnist).pptx)
4. [GoogLeNet](https://github.com/sjchoi86/dl_tutorials/blob/master/presentations/Week2-1b%20GoogLeNet.pptx)
5. [AlphaGo: MCTS+CNN](https://github.com/sjchoi86/dl_tutorials/blob/master/presentations/Week2-1c%20AlphaGo.pptx)
6. [Let's implement MLP!](https://github.com/sjchoi86/dl_tutorials/blob/master/presentations/Week2-1d%20Multi-layer%20perceptron%20(mlp_mnist_simple).pptx)
7. [Let's play with you OWN DATASET](https://github.com/sjchoi86/dl_tutorials/blob/master/presentations/Week2-1e%20Generate%20your%20own%20dataset%20(basic_gendataset).pptx)
8. [Regularization methods](https://github.com/sjchoi86/dl_tutorials/blob/master/presentations/Week2-1f%20Regulaziation.pptx)
9. [Optimization methods](https://github.com/sjchoi86/dl_tutorials/blob/master/presentations/Week2-2a%20Optimizaiton%20methods.pptx)
10. [Restricted Boltzmann Machine](https://github.com/sjchoi86/dl_tutorials/blob/master/presentations/Week2-2b%20Restricted%20Boltzmann%20machine.pptx)
11. [Let's implement denoising autoencoder](https://github.com/sjchoi86/dl_tutorials/blob/master/presentations/Week2-2c%20Denoising%20auto-encoder%20(dae_mnist).pptx)

## Week3 - CNN basics
1. [Semantic segmentation: FCN, DeconvNet, DeepLab with atrous conv](https://github.com/sjchoi86/dl_tutorials/blob/master/presentations/Week3-1b%20Semantic%20segmentation%20details%2BSOTA.pptx)
2. [Let's implement a simple CNN](https://github.com/sjchoi86/dl_tutorials/blob/master/presentations/Week3-1c%20What%20is%20CNN%20(cnn_mnist_simple).pptx)
3. [Let's implement a basic CNN](https://github.com/sjchoi86/dl_tutorials/blob/master/presentations/Week3-1d%20Powerful%20CNN%20(cnn_mnist_basic).pptx)
4. [Let's implement semantic segmentation](https://github.com/sjchoi86/dl_tutorials/blob/master/presentations/Week3-1e%20Implementing%20semantic%20segmentation%20(semseg_basic).pptx) 
5. [Weakly supervised localization: Global average pooling](https://github.com/sjchoi86/dl_tutorials/blob/master/presentations/Week3-2a%20Weakly%20supervised%20learning.pptx)
6. [Implement MLP and CNN on your OWN DATASET](https://github.com/sjchoi86/dl_tutorials/blob/master/presentations/Week3-2b%20Use%20your%20own%20dataset%20(basic_gendataset%2C%20lr%2C%20mlp%2C%20cnn).pptx)
7. [Denoising deconvolutional neural network](https://github.com/sjchoi86/dl_tutorials/blob/master/presentations/Week3-2c%20Denoising%20deconvolutional%20network.pptx)

## Week4 - CNN applications + RNN basics
1. [Image detection (RCNN, SPPnet, FastRCNN, FasterRCNN)](https://github.com/sjchoi86/dl_tutorials/blob/master/presentations/Week4-1a%20Image%20detection%20(RCNN%2C%20SPPnet%2C%20FastRCNN%2C%20FasterRCNN).pptx)
2. [Other detections (YOLO, AttentionNet)](https://github.com/sjchoi86/dl_tutorials/blob/master/presentations/Week4-1b%20Other%20dections%20(YOLO%2C%20AttentionNet).pptx)
3. [Let's use TensorBoards](https://github.com/sjchoi86/dl_tutorials/blob/master/presentations/Week4-1c%20TensorBoard.pptx)
4. [RNN from Colah's blog](https://github.com/sjchoi86/dl_tutorials/blob/master/presentations/Week4-2a%20RNN%20(colah%20blog).pptx)
5. [Visual QnQ: DPPnet + MCBP!](https://github.com/sjchoi86/dl_tutorials/blob/master/presentations/Week4-2b%20Visual%20QnA.pptx)
6. [Super resolution](https://github.com/sjchoi86/dl_tutorials/blob/master/presentations/Week4-2c%20Super%20resolution.pptx)
7. [Deep reinforcement learning](https://github.com/sjchoi86/dl_tutorials/blob/master/presentations/Week4-2d%20Deep%20reinforcement%20learning.pptx)

## Week5 - RNN applications
1. [RNN basic + handwriting generation](https://github.com/sjchoi86/dl_tutorials/blob/master/presentations/Week5-1a%20RNN%20%2B%20LSTM%20%2B%20Handwrting%20Gen.pptx)
2. [Let's implement RNNs](https://github.com/sjchoi86/dl_tutorials/blob/master/presentations/Week5-1b%20Implementing%20RNN%20(rnn_mnist_simple).pptx)
3. [Let's implement Word2vec](https://github.com/sjchoi86/dl_tutorials/blob/master/presentations/Week5-2a%20Word2Vec%20again.pptx)
4. [Image captioning: Show and Tell + Show, attend and tell](https://github.com/sjchoi86/dl_tutorials/blob/master/presentations/Week5-2b%20Image%20Captioning.pptx)
5. [char-rnn + how can we use Hangul?](https://github.com/sjchoi86/dl_tutorials/blob/master/presentations/Week5-2c%20Hangul-RNN.pptx)

## Week6 - Deep learning is so FUN!
1. [Residual network and some analysis](https://github.com/sjchoi86/dl_tutorials/blob/master/presentations/Week6-1a%20Residual%20Networks%20and%20Analysis.pptx)
2. [Neural Style: Texture synsthesis+Inverting CNN](https://github.com/sjchoi86/dl_tutorials/blob/master/presentations/Week6-1b%20Neural%20Style.pptx)
3. [Let's implement neural style](https://github.com/sjchoi86/dl_tutorials/blob/master/presentations/Week6-1c%20Neural%20Style%20Code.pptx)
4. [Bayesian optimization](https://github.com/sjchoi86/dl_tutorials/blob/master/presentations/Week6-2a%20Bayesian%20Optimization.pptx)
5. [Adversaral attack?](https://github.com/sjchoi86/dl_tutorials/blob/master/presentations/Week6-2b%20Adversarial%20Attack.pptx)
6. [Generative adversarial network](https://github.com/sjchoi86/dl_tutorials/blob/master/presentations/Week6-2c%20Generative%20Adversarial%20Network.pptx)