Book Image

Python: Deeper Insights into Machine Learning

By : David Julian, Sebastian Raschka, John Hearty
Book Image

Python: Deeper Insights into Machine Learning

By: David Julian, Sebastian Raschka, John Hearty

Overview of this book

Machine learning and predictive analytics are becoming one of the key strategies for unlocking growth in a challenging contemporary marketplace. It is one of the fastest growing trends in modern computing, and everyone wants to get into the field of machine learning. In order to obtain sufficient recognition in this field, one must be able to understand and design a machine learning system that serves the needs of a project. The idea is to prepare a learning path that will help you to tackle the real-world complexities of modern machine learning with innovative and cutting-edge techniques. Also, it will give you a solid foundation in the machine learning design process, and enable you to build customized machine learning models to solve unique problems. The course begins with getting your Python fundamentals nailed down. It focuses on answering the right questions that cove a wide range of powerful Python libraries, including scikit-learn Theano and Keras.After getting familiar with Python core concepts, it’s time to dive into the field of data science. You will further gain a solid foundation on the machine learning design and also learn to customize models for solving problems. At a later stage, you will get a grip on more advanced techniques and acquire a broad set of powerful skills in the area of feature selection and feature engineering.
Table of Contents (6 chapters)
4
A. Biblography
5
Index

Chapter 6. Neural Networks

Artificial neural networks, as the name suggests, are based algorithms that attempt to mimic the way neurons work in the brain. Conceptual work began in the 1940s, but it is only somewhat recently that a number of important insights, together with the availability of hardware to run these more computationally expensive models, have given neural networks practical application. They are now state-of-the-art techniques that are at the heart of many advanced machine learning applications.

In this chapter, we will introduce the following topics:

  • Logistic units
  • The cost function for neural networks
  • Implementing a neural network
  • Other neural network architectures

Getting started with neural networks

We saw in the last chapter how we could create a nonlinear decision boundary by adding polynomial terms to our hypothesis function. We can also use this technique in linear regression to fit nonlinear data. However, this is not the ideal solution for a number of reasons...