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)

NLP and sentiment analysis

NLP is a subfield in artificial intelligence (AI) that is concerned with the interaction of computers and human languages. As early as the 1950s, scientists were interested in designing intelligent machines that could understand human languages. Early efforts to create a language translator focused on the rule-based approach, where a group of linguistic experts handcrafted a set of rules to be encoded in machines. However, this rule-based approach produced results that were sub-optimal, and, often, it was impossible to convert these rules from one language to another, which meant that scaling up was difficult. For many decades, not much progress was made in NLP, and human language was a goal that AI couldn't reach—until the resurgence of deep learning.

With the proliferation of deep learning and neural networks in the image classification...