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 in New York City

Yellow cabs in NYC are perhaps one of the most recognizable icons in the city. Tens of thousands of commuters in NYC rely on taxis as a mode of transportation around the bustling metropolis. In recent years, the taxi industry in NYC has been put under increasing pressure from ride-hailing apps such as Uber.

In order to rise to the challenge from ride-hailing apps, yellow cabs in NYC are looking to modernize their operations, and to provide a user experience on par with Uber. In August 2018, the Taxi and Limousine Commission of NYC launched a new app that allows commuters to book a yellow cab from their phones. The app provides fare pricing upfront before they hail a cab. Creating an algorithm to provide fare pricing upfront is no simple feat. The algorithm needs to consider various environmental variables such as traffic conditions, time...