- 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
Now that we're at the end of this chapter, it's time to put what we've learned into use. Using the knowledge that you've gained in this chapter, why not try the following exercises?
- Try to build any model that features exploding gradients in training. Hint: Do not normalize inputs and play with the initialization of layers.
- Go to any example in this book and try to optimize performance by improving the data pipeline.
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