The code for this repository is under development 👷

How to run this code

The code in this repository is quite compute heavy and best run on a GPU enabled machine. The datascience platform Kaggle offers free GPU recourses together with free online Jupyter notebooks. To make edits on the Kaggle notebooks, click 'Fork' to create a new copy of the notebook. You will need a Kaggle account for this.

Alternatively you can just view the notebooks on NB Viewer or download the code and run it locally.

Chapter 1 - A neural Network from Scratch

A neural network from Scratch & Intro to Keras: Run on Kaggle, View Online

Excercise excel sheet: Download

Chapter 2 - Structured Data

Credit card fraud detection: Run On Kaggle, View Online

Chapter 3 - Computer Vision Building Blocks

Classifying MNIST digits: Run On Kaggle, View Online

Chapter 4 - Practical Computer Vision

Classifying Plants: View Online, Run On Colab

Intro to Python Generators: Run On Kaggle

Keras Generator with Logistic Regression: Run On Kaggle

Stacking VGG: Run On Kaggle

Preprocessing and Saving VGG Outputs: Run On Kaggle

Rule Based Preprocessing and Augmentation: Run On Kaggle

Visualizing ConvNets: Run On Kaggle

Chapter 5 - Time Series

Forecasting Web Traffic: Classic Methods: Run On Kaggle, View Online

Forecasting Web Traffic: Time Series Neural Nets: Run On Kaggle, View Online

Expressing Uncertainty with Bayesian Deep Learning: Run On Kaggle, View Online

Chapter 6 - Natural Language processing

Analyzing the News: Run On Kaggle, View Online

Classifying Tweets: Run On Kaggle, View Online

Topic modeling with LDA: Run On Kaggle, View Online

Sequence to Sequence models: Run On Kaggle, View Online

Chapter 7 - Generative Models

(Variational) Autoencoder for MNIST: Run On Kaggle, View Online

(Variational) Autoencoder for Fraud Detection: Run On Kaggle, View Online

MNIST DCGAN: Run On Kaggle, View Online

Semi Supervised Generative Adversarial Network for Fraud Detection: Run On Kaggle, View Online

Chapter 8 - Reinforcement Learning

Q-Learning: View Online

A2C Pole Balancing: View Online

A2C for Trading: Run On Kaggle View Online

Chapter 9 - Debugging ML Systems

Unit Testing Data: Run On Kaggle, View Online

Hyperparameter Optimization: View Online

Learning Rate Search: View Online

Using Tensorboard: View Online

Converting Keras Models to TF Estimators: View Online

Faster Python with Cython: Download Part 1, Download Part 2

Chapter 10 - Fighting Bias in Machine Learning

Understanding Parity in Excel: Download

Learning How to Pivot: View Online

Interpretability with SHAP: View Online

IsaGrue / Machine-Learning-for-Finance