- Machine Learning for Finance
- Table of Contents
- Machine Learning for Finance
- Contributors
- Preface
- 1. Neural Networks and Gradient-Based Optimization
- Our journey in this book
- What is machine learning?
- Supervised learning
- Unsupervised learning
- Reinforcement learning
- Setting up your workspace
- Using Kaggle kernels
- Using the AWS deep learning AMI
- Approximating functions
- A forward pass
- A logistic regressor
- Optimizing model parameters
- Measuring model loss
- A deeper network
- A brief introduction to Keras
- Tensors and the computational graph
- Exercises
- Summary
- 2. Applying Machine Learning to Structured Data
- 3. Utilizing Computer Vision
- 4. Understanding Time Series
- Visualization and preparation in pandas
- Different kinds of stationarity
- Fast Fourier transformations
- Autocorrelation
- Establishing a training and testing regime
- A note on backtesting
- Median forecasting
- ARIMA
- Kalman filters
- Forecasting with neural networks
- Conv1D
- Dilated and causal convolution
- Simple RNN
- LSTM
- Recurrent dropout
- Bayesian deep learning
- Exercises
- Summary
- 5. Parsing Textual Data with Natural Language Processing
- An introductory guide to spaCy
- Named entity recognition
- Part-of-speech (POS) tagging
- Rule-based matching
- Regular expressions
- A text classification task
- Preparing the data
- Bag-of-words
- Topic modeling
- Word embeddings
- Document similarity with word embeddings
- A quick tour of the Keras functional API
- Attention
- Seq2seq models
- Exercises
- Summary
- 6. Using Generative Models
- 7. Reinforcement Learning for Financial Markets
- 8. Privacy, Debugging, and Launching Your Products
- 9. Fighting Bias
- 10. Bayesian Inference and Probabilistic Programming
- Other Books You May Enjoy
- Index
To get more comfortable with generative models, try your hand at these exercises:
- Create an SGAN in order to train an MNIST image classifier. How few images can you use to achieve over 90% classification accuracy?
- Using LSTMs, you can build an autoencoder for stock price movements. Using a dataset such as the DJIA stock prices, build an autoencoder that encodes stock movements. Then visualize what happens to the outputs as you move through the latent space. You can find the dataset here: https://www.kaggle.com/szrlee/stock-time-series-20050101-to-20171231.
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