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.
A neural network from Scratch & Intro to Keras: Run on Kaggle, View Online
Excercise excel sheet: Download
Credit card fraud detection: Run On Kaggle, View Online
Classifying MNIST digits: Run On Kaggle, View Online
Classifying Plants: View Online Run On Colab
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
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
(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
Q-Learning: View Online
A2C Pole Balancing: View Online
A2C for Trading: Run On Kaggle View Online
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
Understanding Parity in Excel: Download
Learning How to Pivot: View Online
Interpretability with SHAP: View Online