Book Image

Neural Network Projects with Python

By : James Loy
Book Image

Neural Network Projects with Python

By: James Loy

Overview of this book

Neural networks are at the core of recent AI advances, providing some of the best resolutions to many real-world problems, including image recognition, medical diagnosis, text analysis, and more. This book goes through some basic neural network and deep learning concepts, as well as some popular libraries in Python for implementing them. It contains practical demonstrations of neural networks in domains such as fare prediction, image classification, sentiment analysis, and more. In each case, the book provides a problem statement, the specific neural network architecture required to tackle that problem, the reasoning behind the algorithm used, and the associated Python code to implement the solution from scratch. In the process, you will gain hands-on experience with using popular Python libraries such as Keras to build and train your own neural networks from scratch. By the end of this book, you will have mastered the different neural network architectures and created cutting-edge AI projects in Python that will immediately strengthen your machine learning portfolio.
Table of Contents (10 chapters)

Predicting Taxi Fares with Deep Feedforward Networks

In this chapter, we will use a deep feedforward neural network to predict taxi fares in New York City (NYC), given inputs such as the pickup and drop off locations.

In the previous chapter, Chapter 2, Predicting Diabetes with Multilayer Perceptrons, we saw how we can use a MLP with two hidden layers to perform a classification task (whether the patient is at risk of diabetes or not). In this chapter, we will build a deep neural network to perform a regression task of estimating taxi fares. As we shall see, we will need a deeper (that is, more complex) neural network to achieve this goal.

In this chapter, we will cover the following topics:

  • The motivation for the problem that we're trying to tackle—making accurate predictions of taxi fares
  • Classification versus regression problems in machine learning
  • In-depth analysis...