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Lesson 0 - LinRegression.ipynb
Lesson 0 - LinRegression.ipynb
 
 
Lesson 01 - PenalisedRegression - Solutions.ipynb
Lesson 01 - PenalisedRegression - Solutions.ipynb
 
 
Lesson 01 - PenalisedRegression.ipynb
Lesson 01 - PenalisedRegression.ipynb
 
 
Lesson 02 - GradientDescent.ipynb
Lesson 02 - GradientDescent.ipynb
 
 
Lesson 03 - TF_intro.ipynb
Lesson 03 - TF_intro.ipynb
 
 
Lesson 04 - NeuralNets.ipynb
Lesson 04 - NeuralNets.ipynb
 
 
Lesson 04 - Part 2 - Keras Neural Nets.ipynb
Lesson 04 - Part 2 - Keras Neural Nets.ipynb
 
 
Lesson 05 - Keras Sentiment Analysis - Solutions.ipynb
Lesson 05 - Keras Sentiment Analysis - Solutions.ipynb
 
 
Lesson 05 - Keras Sentiment Analysis.ipynb
Lesson 05 - Keras Sentiment Analysis.ipynb
 
 
Lesson 06 - contraception.ipynb
Lesson 06 - contraception.ipynb
 
 
Lesson 07 - Word2vec.ipynb
Lesson 07 - Word2vec.ipynb
 
 
Lesson 08 - Bike Sharing - Solutions.ipynb
Lesson 08 - Bike Sharing - Solutions.ipynb
 
 
Lesson 08 - Bike Sharing.ipynb
Lesson 08 - Bike Sharing.ipynb
 
 
Lesson 09 - Hyperparameters - Solutions.ipynb
Lesson 09 - Hyperparameters - Solutions.ipynb
 
 
Lesson 09 - Hyperparameters.ipynb
Lesson 09 - Hyperparameters.ipynb
 
 
Lesson 10 - XGBoost + Scikit learn - Solutions.ipynb
Lesson 10 - XGBoost + Scikit learn - Solutions.ipynb
 
 
Lesson 10 - XGBoost + Scikit learn.ipynb
Lesson 10 - XGBoost + Scikit learn.ipynb
 
 
Lesson 11 - Convolutional Neural Nets - Solutions.ipynb
Lesson 11 - Convolutional Neural Nets - Solutions.ipynb
 
 
Lesson 11 - Convolutional Neural Nets.ipynb
Lesson 11 - Convolutional Neural Nets.ipynb
 
 
Lesson 12 - CIFAR10 CNN - Solutions.ipynb
Lesson 12 - CIFAR10 CNN - Solutions.ipynb
 
 
Lesson 12 - CIFAR10 CNN.ipynb
Lesson 12 - CIFAR10 CNN.ipynb
 
 
Lesson 13 - Transfer Learning - Solutions.ipynb
Lesson 13 - Transfer Learning - Solutions.ipynb
 
 
Lesson 13 - Transfer Learning.ipynb
Lesson 13 - Transfer Learning.ipynb
 
 
Lesson 14 - Sentiment Analysis - RNNs - Solutions.ipynb
Lesson 14 - Sentiment Analysis - RNNs - Solutions.ipynb
 
 
Lesson 14 - Sentiment Analysis - RNNs.ipynb
Lesson 14 - Sentiment Analysis - RNNs.ipynb
 
 
Lesson 15 - LSTM Shakespeare generator - Solutions.ipynb
Lesson 15 - LSTM Shakespeare generator - Solutions.ipynb
 
 
Lesson 15 - LSTM Shakespeare generator.ipynb
Lesson 15 - LSTM Shakespeare generator.ipynb
 
 
Lesson 16 - LSTM Trump Tweets - Solutions.ipynb
Lesson 16 - LSTM Trump Tweets - Solutions.ipynb
 
 
Lesson 16 - LSTM Trump Tweets.ipynb
Lesson 16 - LSTM Trump Tweets.ipynb
 
 
Lesson 17 - LSTM part 2 Trump Tweets - Solutions.ipynb
Lesson 17 - LSTM part 2 Trump Tweets - Solutions.ipynb
 
 
Lesson 17 - LSTM part 2 Trump Tweets.ipynb
Lesson 17 - LSTM part 2 Trump Tweets.ipynb
 
 
Lesson 18 - Fake News Classification - Solutions.ipynb
Lesson 18 - Fake News Classification - Solutions.ipynb
 
 
Lesson 18 - Fake News Classification.ipynb
Lesson 18 - Fake News Classification.ipynb
 
 
Lesson 19 - Seq2Seq - Date translator - Solutions - bLSTM.ipynb
Lesson 19 - Seq2Seq - Date translator - Solutions - bLSTM.ipynb
 
 
Lesson 19 - Seq2Seq - Date translator - Solutions.ipynb
Lesson 19 - Seq2Seq - Date translator - Solutions.ipynb
 
 
Lesson 19 - Seq2Seq - Date translator.ipynb
Lesson 19 - Seq2Seq - Date translator.ipynb
 
 
Lesson 20 - Deep Q Learning - Solutions.ipynb
Lesson 20 - Deep Q Learning - Solutions.ipynb
 
 
Lesson 20 - Deep Q Learning.ipynb
Lesson 20 - Deep Q Learning.ipynb
 
 
Lesson 21 - Generative Adversarial Networks.ipynb
Lesson 21 - Generative Adversarial Networks.ipynb
 
 
Lesson 22 - Final Thoughts.ipynb
Lesson 22 - Final Thoughts.ipynb
 
 
Readme.md
Readme.md
 
 
Reinforcement Learning - Policy Gradients.ipynb
Reinforcement Learning - Policy Gradients.ipynb
 
 
utilities.py
utilities.py
 
 

Readme.md

Deep Learning

All lessons in this folder are in Keras (mainly) and Tensorflow.

Contents

Intro

  1. Lesson 0: Introduction to regression.
  2. Lesson 1: Penalising weights to fit better (scikit learn intro)

Mathematics (optional)

  1. Lesson 2: Gradient Descent. Using basic optimisation methods.
  2. Lesson 3: Tensorflow intro: zero layer hidden networks (i.e. normal regression).
  3. Lesson 4: Tensorflow hidden layer introduction.

Intermediate

  1. Lesson 5: Using Keras to simplify multi layer neural nets.
  2. Lesson 6: Embeddings to deal with categorical data. (Keras)
  3. Lesson 7: Word2Vec. Embeddings to visualise words. (Tensorflow)
  4. Lesson 8: Application - Bike Sharing predictions
  5. Lesson 9: Choosing Number of Layers and more
  6. Lesson 10: XGBoost - A quick detour from Deep Learning
  7. Lesson 11: Convolutional Neural Nets (MNIST dataset)
  8. Lesson 12: CNNs and BatchNormalisation (CIFAR10 dataset)
  9. Lesson 13: Transfer Learning (Dogs vs Cats dataset)

Novice

  1. Lesson 14: LSTMs - Sentiment analysis.
  2. Lesson 15: LSTMs - Shakespeare.
  3. Lesson 16: LSTMs - Trump Tweets.
  4. Lesson 17: Trump - Stacking and Stateful LSTMs.
  5. Lesson 18: Fake News Classifier

Advanced

  1. Lesson 19: Sequence to Sequence
  2. Lesson 20: Deep Q Learning
  3. Lesson 21: Generative Adversarial Networks