All lessons in this folder are in Keras (mainly) and Tensorflow.
- Lesson 0: Introduction to regression.
- Lesson 1: Penalising weights to fit better (scikit learn intro)
- Lesson 2: Gradient Descent. Using basic optimisation methods.
- Lesson 3: Tensorflow intro: zero layer hidden networks (i.e. normal regression).
- Lesson 4: Tensorflow hidden layer introduction.
- Lesson 5: Using Keras to simplify multi layer neural nets.
- Lesson 6: Embeddings to deal with categorical data. (Keras)
- Lesson 7: Word2Vec. Embeddings to visualise words. (Tensorflow)
- Lesson 8: Application - Bike Sharing predictions
- Lesson 9: Choosing Number of Layers and more
- Lesson 10: XGBoost - A quick detour from Deep Learning
- Lesson 11: Convolutional Neural Nets (MNIST dataset)
- Lesson 12: CNNs and BatchNormalisation (CIFAR10 dataset)
- Lesson 13: Transfer Learning (Dogs vs Cats dataset)
- Lesson 14: LSTMs - Sentiment analysis.
- Lesson 15: LSTMs - Shakespeare.
- Lesson 16: LSTMs - Trump Tweets.
- Lesson 17: Trump - Stacking and Stateful LSTMs.
- Lesson 18: Fake News Classifier
- Lesson 19: Sequence to Sequence
- Lesson 20: Deep Q Learning
- Lesson 21: Generative Adversarial Networks