- 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
In this chapter, you have learned a lot about both the technical and non-technical considerations of fairness in machine learning. These exercises will help you think much more deeply about the topic:
- Think about the organization you work for. How is fairness incorporated in your organization? What works well and what could be improved?
- Revisit any of the models developed in this book. Are they fair? How would you test them for fairness?
- Fairness is only one of the many complex issues large models can have. Can you think of an issue in your area of work that could be tackled with the tools discussed in this chapter?
-
No Comment
..................Content has been hidden....................
You can't read the all page of ebook, please click here login for view all page.