- 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 have finished the first chapter in this exciting journey, I've got a challenge for you! You'll find some exercises that you can do that are all themed around what we've covered in this chapter!
So, why not try to do the following:
- Expand the two-layer neural network in Python to three layers.
- Within the GitHub repository, you will find an Excel file called
1 Excel Exercise. The goal is to classify three types of wine by their cultivar data. Build a logistic regressor to this end in Excel. - Build a two-layer neural network in Excel.
- Play around with the hidden layer size and learning rate of the 2-layer neural network. Which options offer the lowest loss? Does the lowest loss also capture the true relationship?
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