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Title: GitHub - Fatty-Ricky/dl_tutorials: Deep learning tutorials (2nd ed.) · GitHub

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Description: Deep learning tutorials (2nd ed.). Contribute to Fatty-Ricky/dl_tutorials development by creating an account on GitHub.

Open Graph Description: Deep learning tutorials (2nd ed.). Contribute to Fatty-Ricky/dl_tutorials development by creating an account on GitHub.

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LICENSE	https://github.com/Fatty-Ricky/dl_tutorials/blob/master/LICENSE
README.md	https://github.com/Fatty-Ricky/dl_tutorials/blob/master/README.md
README.md	https://github.com/Fatty-Ricky/dl_tutorials/blob/master/README.md
README	https://github.com/Fatty-Ricky/dl_tutorials
MIT license	https://github.com/Fatty-Ricky/dl_tutorials
	https://github.com/Fatty-Ricky/dl_tutorials#deep-learning-tutorials
	https://github.com/Fatty-Ricky/dl_tutorials#week1
Deep learning intro.	https://github.com/sjchoi86/dl_tutorials/blob/master/presentations/Week1-1b%20Deep%20learning%20intro.pptx
Python basics	https://github.com/sjchoi86/dl_tutorials/blob/master/presentations/Week1-1c%20Python%20basic%20(basic_python).pptx
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
Terminologies	https://github.com/sjchoi86/dl_tutorials/blob/master/presentations/Week1-2a%20Terminologies.pptx
	https://github.com/Fatty-Ricky/dl_tutorials#week2---do-you-know-deep-learning
CNN and AlexNet	https://github.com/sjchoi86/dl_tutorials/blob/master/presentations/Week1-2b%20CNN%20and%20AlexNet.pptx
TensorFlow basics	https://github.com/sjchoi86/dl_tutorials/blob/master/presentations/Week1-2c%20TensorFlow%20basic%20(basic_tensorflow).pptx
Logistic regression	https://github.com/sjchoi86/dl_tutorials/blob/master/presentations/Week1-2d%20Logistic%20regression%20(logistic_regression_mnist).pptx
GoogLeNet	https://github.com/sjchoi86/dl_tutorials/blob/master/presentations/Week2-1b%20GoogLeNet.pptx
AlphaGo: MCTS+CNN	https://github.com/sjchoi86/dl_tutorials/blob/master/presentations/Week2-1c%20AlphaGo.pptx
Let's implement MLP!	https://github.com/sjchoi86/dl_tutorials/blob/master/presentations/Week2-1d%20Multi-layer%20perceptron%20(mlp_mnist_simple).pptx
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
Regularization methods	https://github.com/sjchoi86/dl_tutorials/blob/master/presentations/Week2-1f%20Regulaziation.pptx
Optimization methods	https://github.com/sjchoi86/dl_tutorials/blob/master/presentations/Week2-2a%20Optimizaiton%20methods.pptx
Restricted Boltzmann Machine	https://github.com/sjchoi86/dl_tutorials/blob/master/presentations/Week2-2b%20Restricted%20Boltzmann%20machine.pptx
Let's implement denoising autoencoder	https://github.com/sjchoi86/dl_tutorials/blob/master/presentations/Week2-2c%20Denoising%20auto-encoder%20(dae_mnist).pptx
	https://github.com/Fatty-Ricky/dl_tutorials#week3---cnn-basics
Semantic segmentation: FCN, DeconvNet, DeepLab with atrous conv	https://github.com/sjchoi86/dl_tutorials/blob/master/presentations/Week3-1b%20Semantic%20segmentation%20details%2BSOTA.pptx
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
Let's implement a basic CNN	https://github.com/sjchoi86/dl_tutorials/blob/master/presentations/Week3-1d%20Powerful%20CNN%20(cnn_mnist_basic).pptx
Let's implement semantic segmentation	https://github.com/sjchoi86/dl_tutorials/blob/master/presentations/Week3-1e%20Implementing%20semantic%20segmentation%20(semseg_basic).pptx
Weakly supervised localization: Global average pooling	https://github.com/sjchoi86/dl_tutorials/blob/master/presentations/Week3-2a%20Weakly%20supervised%20learning.pptx
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
Denoising deconvolutional neural network	https://github.com/sjchoi86/dl_tutorials/blob/master/presentations/Week3-2c%20Denoising%20deconvolutional%20network.pptx
	https://github.com/Fatty-Ricky/dl_tutorials#week4---cnn-applications--rnn-basics
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
Other detections (YOLO, AttentionNet)	https://github.com/sjchoi86/dl_tutorials/blob/master/presentations/Week4-1b%20Other%20dections%20(YOLO%2C%20AttentionNet).pptx
Let's use TensorBoards	https://github.com/sjchoi86/dl_tutorials/blob/master/presentations/Week4-1c%20TensorBoard.pptx
RNN from Colah's blog	https://github.com/sjchoi86/dl_tutorials/blob/master/presentations/Week4-2a%20RNN%20(colah%20blog).pptx
Visual QnA: DPPnet + MCBP!	https://github.com/sjchoi86/dl_tutorials/blob/master/presentations/Week4-2b%20Visual%20QnA.pptx
Super resolution	https://github.com/sjchoi86/dl_tutorials/blob/master/presentations/Week4-2c%20Super%20resolution.pptx
Deep reinforcement learning	https://github.com/sjchoi86/dl_tutorials/blob/master/presentations/Week4-2d%20Deep%20reinforcement%20learning.pptx
	https://github.com/Fatty-Ricky/dl_tutorials#week5---rnn-applications
RNN basic + handwriting generation	https://github.com/sjchoi86/dl_tutorials/blob/master/presentations/Week5-1a%20RNN%20%2B%20LSTM%20%2B%20Handwrting%20Gen.pptx
Let's implement RNNs	https://github.com/sjchoi86/dl_tutorials/blob/master/presentations/Week5-1b%20Implementing%20RNN%20(rnn_mnist_simple).pptx
Let's implement Word2vec	https://github.com/sjchoi86/dl_tutorials/blob/master/presentations/Week5-2a%20Word2Vec%20again.pptx
Image captioning: Show and Tell + Show, attend and tell	https://github.com/sjchoi86/dl_tutorials/blob/master/presentations/Week5-2b%20Image%20Captioning.pptx
char-rnn + how can we use Hangul?	https://github.com/sjchoi86/dl_tutorials/blob/master/presentations/Week5-2c%20Hangul-RNN.pptx
	https://github.com/Fatty-Ricky/dl_tutorials#week6---deep-learning-is-so-fun
Residual network and some analysis	https://github.com/sjchoi86/dl_tutorials/blob/master/presentations/Week6-1a%20Residual%20Networks%20and%20Analysis.pptx
Neural Style: Texture synsthesis+Inverting CNN	https://github.com/sjchoi86/dl_tutorials/blob/master/presentations/Week6-1b%20Neural%20Style.pptx
Let's implement neural style	https://github.com/sjchoi86/dl_tutorials/blob/master/presentations/Week6-1c%20Neural%20Style%20Code.pptx
Bayesian optimization	https://github.com/sjchoi86/dl_tutorials/blob/master/presentations/Week6-2a%20Bayesian%20Optimization.pptx
Adversaral attack?	https://github.com/sjchoi86/dl_tutorials/blob/master/presentations/Week6-2b%20Adversarial%20Attack.pptx
Generative adversarial network	https://github.com/sjchoi86/dl_tutorials/blob/master/presentations/Week6-2c%20Generative%20Adversarial%20Network.pptx
Readme	https://github.com/Fatty-Ricky/dl_tutorials#readme-ov-file
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