Clear your basics of Neural Networks in Machine Learning

This book covers both classical and modern models in deep learning. The primary focus is on the theory and algorithms of deep learning. The theory and algorithms of neural networks are particularly important for understanding important concepts so that one can understand the important design concepts of neural architectures in different applications. Why do neural networks work? When do they work better than off-the-shelf machine-learning models? When is depth useful? Why is training neural networks so hard? What are the pitfalls? The book is also rich in discussing different applications in order to give the practitioner a flavor of how neural architectures are designed for different types of problems. Applications associated with many different areas like recommender systems, machine translation, image captioning, image classification, reinforcement-learning based gaming, and text analytics are covered.

The book is written for graduate students, researchers, and practitioners. Numerous exercises are available along with a solution manual to aid in classroom teaching. Where possible, an application-centric view is highlighted in order to provide an understanding of the practical uses of each class of techniques.

Fundamentals of Neural Networks: Architectures, Algorithms and Applications, 1e Paperback - 2004

by FAUSETT

An exceptionally clear, thorough introduction to neural networks written at an elementary level. Written with the beginning student in mind, the text features systematic discussions of all major neural networks and fortifies the reader's understudy with many examples.

Make Your Own Neural Network [Print Replica] Kindle Edition

by Tariq Rashid

Neural networks are a key element of deep learning and artificial intelligence, which today is capable of some truly impressive feats. Yet too few really understand how neural networks actually work.

This guide will take you on a fun and unhurried journey, starting from very simple ideas, and gradually building up an understanding of how neural networks work. You won't need any mathematics beyond secondary school, and an accessible introduction to calculus is also included.

The ambition of this guide is to make neural networks as accessible as possible to as many readers as possible - there are enough texts for advanced readers already!

You'll learn to code in Python and make your own neural network, teaching it to recognize human handwritten numbers, and performing as well as professionally developed networks.

Neural Networks: A Comprehensive Foundation (2nd Edition) 2nd Edition

by Simon Haykin

Provides a comprehensive foundation of neural networks, recognizing the multidisciplinary nature of the subject, supported with examples, computer-oriented experiments, end of chapter problems, and a bibliography. DLC: Neural networks (Computer science).

Neural Networks with R: Smart models using CNN, RNN, deep learning, and artificial intelligence principles 1st Edition, Kindle Edition

By Giuseppe Ciaburro

About This Book

Develop a strong background in neural networks with R, to implement them in your applications
Build smart systems using the power of deep learning
Real-world case studies to illustrate the power of neural network models

Neural networks are one of the most fascinating machine learning models for solving complex computational problems efficiently. Neural networks are used to solve a wide range of problems in different areas of AI and machine learning.

This book explains the niche aspects of neural networking and provides you with a foundation to get started with advanced topics. The book begins with neural network design using the neural net package, then you'll build a solid foundation knowledge of how a neural network learns from data, and the principles behind it. This book covers various types of the neural network including recurrent neural networks and convoluted neural networks. You will not only learn how to train neural networks but will also explore the generalization of these networks. Later we will delve into combining different neural network models and work with the real-world use cases.

By the end of this book, you will learn to implement neural network models in your applications with the help of practical examples in the book.

Source: HOB

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