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 bike sharing demand

In the previous dataset, the were strongly correlated with the labels. However, in some time series, we have features that might be less correlated or not correlated with the labels at all. The main idea behind machine learning is that the algorithm tries to figure out by itself which features are valuable and which are not. Especially in deep learning, we want to keep the feature engineering limited. In the following recipe, we will be predicting the demand for bike sharing rentals. The data includes some interesting features, such as weather type, holiday, temperature, and season.

How to do it...

  1. First, we all libraries:
from sklearn import preprocessing
import pandas as pd
import numpy as np
from math import pi, sin, cos
from datetime import datetime

from keras.models import Sequential
from keras.layers import Dense, Activation, Dropout
from keras.optimizers import Adam
from keras.callbacks import EarlyStopping

  1. The training and test data is stored in two...