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. How are images represented in computers?

Images are represented in computers as a group of pixels, with each pixel having its own intensity (value between 0 and 255). Color images have three channels (red, green, and blue) while grayscale images have only one channel.

  1. What are the fundamental building blocks of a CNN?

All convolutional neural network consists of convolution layers, pooling layers, and fully connected layers.

  1. What is the role of the convolutional and pooling layers?

The convolutional and pooling layers are responsible for extracting spatial characteristics from the images. For example, when training a CNN to identify images of cats, one such spatial characteristic would be the pointy ears of cats.

  1. What is the role of the fully connected layers?

The fully connected layers are similar to the those in MLPs and feedforward neural networks. Their...