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Hands-On Machine Learning for Algorithmic Trading

You're reading from   Hands-On Machine Learning for Algorithmic Trading Design and implement investment strategies based on smart algorithms that learn from data using Python

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Product type Paperback
Published in Dec 2018
Publisher Packt
ISBN-13 9781789346411
Length 684 pages
Edition 1st Edition
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Authors (2):
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Jeffrey Yau Jeffrey Yau
Author Profile Icon Jeffrey Yau
Jeffrey Yau
Stefan Jansen Stefan Jansen
Author Profile Icon Stefan Jansen
Stefan Jansen
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Table of Contents (23) Chapters Close

Preface 1. Machine Learning for Trading 2. Market and Fundamental Data FREE CHAPTER 3. Alternative Data for Finance 4. Alpha Factor Research 5. Strategy Evaluation 6. The Machine Learning Process 7. Linear Models 8. Time Series Models 9. Bayesian Machine Learning 10. Decision Trees and Random Forests 11. Gradient Boosting Machines 12. Unsupervised Learning 13. Working with Text Data 14. Topic Modeling 15. Word Embeddings 16. Deep Learning 17. Convolutional Neural Networks 18. Recurrent Neural Networks 19. Autoencoders and Generative Adversarial Nets 20. Reinforcement Learning 21. Next Steps 22. Other Books You May Enjoy

How to design and train a CNN using Python

All libraries we introduced in the last chapter provide support for convolutional layers. We are going to illustrate the LeNet5 architecture using the most basic MNIST handwritten digit dataset, and then use AlexNet on CIFAR10, a simplified version of the original ImageNet, to demonstrate the use of data augmentation.

LeNet5 and MNIST using Keras

The original MNIST dataset contains 60,000 images in 28 x 28 pixel resolution, with a single grayscale containing handwritten digits from 0 to 9. A good alternative is the more challenging, but structurally similar, Fashion MNIST dataset, which we encountered in Chapter 12, Unsupervised Learning. See the mnist_with_ffnn_and_lenet5 notebook...

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