To get the most out of this book

All code examples are hosted on Kaggle. You can use Kaggle for free and get access to a GPU, which will enable you to run the example code much faster. If you do not have a very powerful machine with a GPU, it will be much more comfortable to run the code on Kaggle. You can find links to all notebooks on this book's GitHub page: https://github.com/PacktPublishing/Machine-Learning-for-Finance.

This book assumes some working knowledge of mathematical concepts such as linear algebra, statistics, probability theory, and calculus. You do not have to be an expert, however.

Equally, knowledge of Python and some popular data science libraries such as pandas and Matplotlib is assumed.

Download the example code files

You can download the example code files for this book from your account at http://www.packt.com. If you purchased this book elsewhere, you can visit http://www.packt.com/support and register to have the files emailed directly to you.

You can download the code files by following these steps:

1. Log in or register at http://www.packt.com.
2. Select the SUPPORT tab.
3. Click on Code Downloads & Errata.
4. Enter the name of the book in the Search box and follow the on-screen instructions.

Once the file is downloaded, please make sure that you unzip or extract the folder using the latest version of:

• WinRAR / 7-Zip for Windows
• Zipeg / iZip / UnRarX for Mac
• 7-Zip / PeaZip for Linux

We also have other code bundles from our rich catalog of books and videos available at https://github.com/PacktPublishing/. Check them out!

Download the color images

We also provide a PDF file that has color images of the screenshots/diagrams used in this book. You can download it here: http://www.packtpub.com/sites/default/files/downloads/9781789136364_ColorImages.pdf.

Conventions used

There are a number of text conventions used throughout this book.

CodeInText: Indicates code words in the text, database table names, folder names, filenames, file extensions, pathnames, dummy URLs, user input, and Twitter handles. For example; "Mount the downloaded WebStorm-10*.dmg disk image file as another disk in your system."

A block of code is set as follows:

import numpy as np
x_train = np.expand_dims(x_train,-1)
x_test = np.expand_dims(x_test,-1)
x_train.shape

When we wish to draw your attention to a particular part of a code block, the relevant lines or items are set in bold:

from keras.models import Sequential
img_shape = (28,28,1)
model = Sequential()
model.add(Conv2D(6,3,input_shape=img_shape))

Any command-line input or output is written as follows:

Train on 60000 samples, validate on 10000 samples
Epoch 1/10
60000/60000 [==============================] - 22s 374us/step - loss: 7707.2773 - acc: 0.6556 - val_loss: 55.7280 - val_acc: 0.7322

Bold: Indicates a new term, an important word, or words that you see on the screen, for example, in menus or dialog boxes, also appear in the text like this. For example: "Select System info from the Administration panel."

Note

Warnings or important notes appear like this.

Tip

Tips and tricks appear like this.