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)

Questions

  1. What are sequential problems in machine learning?

Sequential problems are a class of problem in machine learning in which the order of the features presented to the model is important for making predictions. Examples of sequential problems include NLP problems (for example, speech and text) and time series problems.

  1. What are some reasons that make it challenging for AI to solve sentiment analysis problems?

Human languages often contain words that have different meanings, depending on the context. It is therefore important for a machine learning model to fully understand the context before making a prediction. Furthermore, sarcasm is common in human languages, which is difficult for an AI-based model to comprehend.

  1. How is an RNN different than a CNN?

RNNs can be thought of as multiple, recursive copies of a single neural network. Each layer in an RNN passes its...