Book Image

Python Deep Learning Cookbook

By : Indra den Bakker
Book Image

Python Deep Learning Cookbook

By: Indra den Bakker

Overview of this book

Deep Learning is revolutionizing a wide range of industries. For many applications, deep learning has proven to outperform humans by making faster and more accurate predictions. This book provides a top-down and bottom-up approach to demonstrate deep learning solutions to real-world problems in different areas. These applications include Computer Vision, Natural Language Processing, Time Series, and Robotics. The Python Deep Learning Cookbook presents technical solutions to the issues presented, along with a detailed explanation of the solutions. Furthermore, a discussion on corresponding pros and cons of implementing the proposed solution using one of the popular frameworks like TensorFlow, PyTorch, Keras and CNTK is provided. The book includes recipes that are related to the basic concepts of neural networks. All techniques s, as well as classical networks topologies. The main purpose of this book is to provide Python programmers a detailed list of recipes to apply deep learning to common and not-so-common scenarios.
Table of Contents (21 chapters)
Title Page
About the Author
About the Reviewer
Customer Feedback

Predicting stock prices with neural networks

When machine became popular, there was a lot of attention given to predicting stock prices. Many different algorithms have been applied to predict stock prices, from more traditional algorithms such as random forests to the more recent extreme gradient boosting. While the latter might still outperform deep learning approaches in most cases, it can still be of valuable to use in a neural network approach. This can, for example, be used in an ensemble of networks or for multi-layer stacking. In the following recipe, we will predict stock prices with the Keras framework.

How to do it...

  1. We start by importing all the libraries, as follows:
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from sklearn.preprocessing import MinMaxScaler

from keras.layers.core import Dense, 
Activation, Dropout
from keras.layers.recurrent import LSTM
from keras.models import Sequential
  1. Let's load the data and print the first rows:
data = pd.read_csv...