Deep Learning: Theory and Experiments

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                       	 	<h1 id="articleHeader0"><a id="user-content-deep-learning-theory-and-experiments" class="anchor" aria-hidden="true" href="#deep-learning-theory-and-experiments">

Deep Learning: Theory and Experiments

Sample codes have links to other repo that I maintain (Advanced Deep Learning with Keras book) or contribute (Keras)

Lecture Notes

0. Course Roadmap

• Deep Learning
• Limits of Deep Learning

1. Background Materials

• Linear Algebra
• Probability
• Numerical Computation

2. Machine Learning Basics

• Concepts, Capacity, Estimators, Linear Regression
• MLE, Bayesian, Other ML Algorithms
• Stochastic Gradient Descent, etc

3. Deep Neural Networks

• Deep Feedforward Neural Networks, Cost, Output, Hidden Units
• Back Propagation
• Keras Sample Codes
  • Overview
  • MLP on Linear Model
  • MLP on MNIST
  • MNIST Sampler

4. Regularization

• Keras Sample Codes
  • MLP on MNIST No Regularizer
  • MLP on MNIST with L2 Regularizer
  • MLP on MNIST with Dropout Regularizer
  • MLP on MNIST with Data Augmentation Regularizer

5. Optimization

6. Convolutional Neural Networks (CNN)

• Keras Sample Codes
  • CNN on MNIST
  • CNN using functional API
  • CNN Siamese Network

7. Deep Networks

• Keras Sample Codes
  • DenseNet
  • ResNet

8. Embeddings
9. Recurrent Neural Networks, LSTM, GRU

• Keras Sample Code
  • Sequence to Sequence for Machine Translation (Filipino-English)
• Utils for Testing Sentence Similarity
  • BLEU
  • Spacy

10. AutoEncoders

• Keras Sample Code
  • AutoEncoder
  • Denoising AutoEncoder
  • Colorization AutoEncoder

11. Generative Adversarial Networks (GAN)

• Keras Sample Code
  • DCGAN
  • CGAN

12. Variational Autoencoder (VAE)

• Keras Sample Codes
  • VAE MLP
  • VAE CNN
  • CVAE

13. Deep Reinforcement Learning (DRL)

• Keras Sample Codes
  • Q-Learning
  • Q-Learning on FrozenLake-v0
  • DQN and DDQN on CartPole-v0

Warning: The following are old experiments that are longer updated and maintained

Tensorflow Experiments

1. Hello World!
2. Linear Algebra
3. Matrix Decomposition
4. Probability Distributions using TensorBoard
5. Linear Regression by PseudoInverse
6. Linear Regression by Gradient Descent
7. Under Fitting in Linear Regression
8. Optimal Fitting in Linear Regression
9. Over Fitting in Linear Regression
10. Nearest Neighbor
11. Principal Component Analysis
12. Logical Ops by a 2-layer NN (MSE)
13. Logical Ops by a 2-layer NN (Cross Entropy)
14. NotMNIST Deep Feedforward Network: Code for NN and Code for Pickle
15. NotMNIST CNN
16. word2vec
17. Word Prediction/Story Generation using LSTM. Belling the Cat by Aesop Sample Text Story

Keras on Tensorflow Experiments

1. NotMNIST Deep Feedforward Network
2. NotMNIST CNN
3. DCGAN on MNIST