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1 A neural network from scratch & Intro to Keras.ipynb
1 A neural network from scratch & Intro to Keras.ipynb
 
 
1 Excel Exercise.xlsx
1 Excel Exercise.xlsx
 
 
10.1 Introduction.ipynb
10.1 Introduction.ipynb
 
 
10.2 Markov Monte Carlo.ipynb
10.2 Markov Monte Carlo.ipynb
 
 
10.3 PYMC3.ipynb
10.3 PYMC3.ipynb
 
 
2 structured data.ipynb
2 structured data.ipynb
 
 
3.1 MNIST.ipynb
3.1 MNIST.ipynb
 
 
3.2 Plant Classification.ipynb
3.2 Plant Classification.ipynb
 
 
4.1 EDA & Classic methods.ipynb
4.1 EDA & Classic methods.ipynb
 
 
4.2 NN on time series.ipynb
4.2 NN on time series.ipynb
 
 
4.3 Bayesian Deep Learning.ipynb
4.3 Bayesian Deep Learning.ipynb
 
 
5.1 Analyzing the news.ipynb
5.1 Analyzing the news.ipynb
 
 
5.2 Classifying Tweets.ipynb
5.2 Classifying Tweets.ipynb
 
 
5.3 Topic Modeling.ipynb
5.3 Topic Modeling.ipynb
 
 
5.4 Translation.ipynb
5.4 Translation.ipynb
 
 
6.1 MNIST examples.ipynb
6.1 MNIST examples.ipynb
 
 
6.2 Fraud examples.ipynb
6.2 Fraud examples.ipynb
 
 
6.3 MNIST DCGAN.ipynb
6.3 MNIST DCGAN.ipynb
 
 
6.4 SGAN.ipynb
6.4 SGAN.ipynb
 
 
7.1 Q-Learning.ipynb
7.1 Q-Learning.ipynb
 
 
7.2 A2C Balance.ipynb
7.2 A2C Balance.ipynb
 
 
7.3 A2C Trading.ipynb
7.3 A2C Trading.ipynb
 
 
8.1 Unit Testing Data.ipynb
8.1 Unit Testing Data.ipynb
 
 
8.2 Hyperopt.ipynb
8.2 Hyperopt.ipynb
 
 
8.3 Tensorboard debugging.py
8.3 Tensorboard debugging.py
 
 
8.4 LR_Search.ipynb
8.4 LR_Search.ipynb
 
 
8.5 Tensorboard.ipynb
8.5 Tensorboard.ipynb
 
 
8.6 TF Estimator.ipynb
8.6 TF Estimator.ipynb
 
 
8_7_cython_setup.py
8_7_cython_setup.py
 
 
9.1_parity.xlsx
9.1_parity.xlsx
 
 
9.2_Learning_to_be_fair.ipynb
9.2_Learning_to_be_fair.ipynb
 
 
9.3_SHAP.ipynb
9.3_SHAP.ipynb
 
 
README.md
README.md
 
 
Software and hardware list.pdf
Software and hardware list.pdf
 
 
cython_fib_8_7.pyx
cython_fib_8_7.pyx
 
 

README.md

Machine Learning for Finance

This is the code repository for Machine Learning for Finance, published by Packt. It contains all the supporting project files necessary to work through the book from start to finish.

About the book

Machine Learning for Finance explores new advances in machine learning and shows how they can be applied in the financial sector. It explains the concepts and algorithms behind the main machine learning techniques and provides example Python code for implementing the models yourself.

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 - Neural Networks and Gradient-Based Optimization

Chapter 2 - Applying Machine Learning to Structured Data

Chapter 3 - Utilizing Computer Vision

Chapter 4 - Understanding Time Series

Chapter 5 - Parsing Textual Data with Natural Language processing

Chapter 6 - Using Generative Models

Chapter 7 - Reinforcement Learning for Financial Markets

Chapter 8 - Privacy, Debugging, and Launching Your Products

Chapter 9 - Fighting Bias in Machine Learning

Chapter 10 - Bayesian Inference and Probabilistic Programming