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

Using a shallow neural network for binary classification

Throughout this book, we've on giving ready-to-use for real-world problems. For some relatively simple tasks, a simple neural network can provide a good-enough solution to a problem. In this recipe, we'll demonstrate how straightforward it can be to implement a shallow neural network for binary classification in Keras.

How to do it...

  1. Start with importing all libraries as follows:
import numpy as np
import pandas as pd
from sklearn.preprocessing import LabelEncoder

from keras.models import Sequential
from keras.layers import Dense
from keras.callbacks import EarlyStopping
  1. Next, we load the dataset:
dataframe = pandas.read_csv("Data/sonar.all-data", 
data = dataframe.values
  1. Let's split the labels from the features:
X = data[:,0:60].astype(float)
y = data[:,60]
  1. Currently, the labels are strings. We need to binarize them for our network:
encoder = LabelEncoder()
y = encoder.transform(y)
  1. Let's define a simple network...