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Neural Network Projects with Python

Neural Network Projects with Python

By : James Loy
4.6 (15)
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Neural Network Projects with Python

Neural Network Projects with Python

4.6 (15)
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)
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Computer vision and object recognition

Computer vision is an engineering field where the objective is to create programs that can extract meaning from images. According to an urban legend, computer vision first started in the 1960s when Professor Marvin Minsky from MIT assigned a summer project to a group of undergraduates, with the requirement that they should attach a camera to a computer and to have the computer describe everything that it sees. The project was expected to be completed in just one summer. Needless to say, it wasn't completed within that summer as computer vision is an extremely complex field that scientists are continuously working on even today.

Early progression in computer vision was modest. In the 1960s, researchers started by creating algorithms to detect shapes, lines, and edges in photographs. The following decades saw the evolution of computer...

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