Best Books to Learn Neural Networks

Here is the list of Best Books to Learn Neural networks. We have filtered some of the Best Neural networks Books based on the Reader’s reviews, Concepts and reviews. Here you can find the Best books to learn Neural networks based on your concepts. If you find any Neural Networks books are missing, then comment in the comment section.

7+ Best Books to Learn Neural Networks to read in 2023

Neural Networks and Deep Learning: A Textbook

This book covers both classical and modern models of 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.

The book is also rich in discussing different applications in order to give the practitioner a flavour 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. 

Many traditional machine learning models can be understood as special cases of neural networks.  An emphasis is placed in the first two chapters on understanding the relationship between traditional machine learning and neural networks. Support vector machines, linear/logistic regression, singular value decomposition, matrix factorization, and recommender systems are shown to be special cases of neural networks. These methods are studied together with recent feature engineering methods like word2vec.

The book is written for graduate students, researchers, and practitioners.

Pros:

• With average knowledge of calculus and linear algebra, the math of deep learning is easily understood in the book with the great intuition the author builds prior to the mathematical equations.
• There are several places in the book where connections are drawn between neural networks and how they simulate linear regression, logistic regression and SVMs.

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Neural Network Methods in Natural Language Processing

Neural networks are a family of powerful machine learning models and this book focuses on their application to natural language data.

The first half of the book (Parts I and II) covers the basics of supervised machine learning and feed-forward neural networks, the basics of working with machine learning over language data, and the use of vector-based rather than symbolic representations for words.

It also covers the computation-graph abstraction, which allows to easily define and train arbitrary neural networks, and is the basis behind the design of contemporary neural network software libraries.

The second part of the book (Parts III and IV) introduces more specialized neural network architectures, including 1D convolutional neural networks, recurrent neural networks, conditioned-generation models, and attention-based models.

These architectures and techniques are the driving force behind state-of-the-art algorithms for machine translation, syntactic parsing, and many other applications. Finally, we also discuss tree-shaped networks, structured prediction, and the prospects of multi-task learning.

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Neural Network Projects with Python: The ultimate guide to using Python to explore the true power of neural networks through six projects

Rating: 4.6/5

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.

What you will learn

• Learn various neural network architectures and its advancements in AI
• Master deep learning in Python by building and training neural network
• Master neural networks for regression and classification
• Discover convolutional neural networks for image recognition
• Learn sentiment analysis on textual data using Long Short-Term Memory
• Build and train a highly accurate facial recognition security system

Who this book is for

This book is a perfect match for data scientists, machine learning engineers, and deep learning enthusiasts who wish to create practical neural network projects in Python. Readers should already have some basic knowledge of machine learning and neural networks.

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Java Deep Learning Cookbook: Train neural networks for classification, NLP, and reinforcement learning using Deeplearning4j 

Rating: 4.6/5

Java is one of the most widely used programming languages in the world. With this book, you will see how to perform deep learning using Deeplearning4j (DL4J) – the most popular Java library for training neural networks efficiently.

This book starts by showing you how to install and configure Java and DL4J on your system. You will then gain insights into deep learning basics and use your knowledge to create a deep neural network for binary classification from scratch.

As you progress, you will discover how to build a convolutional neural network (CNN) in DL4J, and understand how to construct numeric vectors from text. This deep learning book will also guide you through performing anomaly detection on unsupervised data and help you set up neural networks in distributed systems effectively.

In addition to this, you will learn how to import models from Keras and change the configuration in a pre-trained DL4J model. Finally, you will explore benchmarking in DL4J and optimize neural networks for optimal results.

By the end of this book, you will have a clear understanding of how you can use DL4J to build robust deep learning applications in Java.

What you will learn

• Perform data normalization and wrangling using DL4J
• Build deep neural networks using DL4J
• Implement CNNs to solve image classification problems
• Train autoencoders to solve anomaly detection problems using DL4J
• Perform benchmarking and optimization to improve your model’s performance
• Implement reinforcement learning for real-world use cases using RL4J
• Leverage the capabilities of DL4J in distributed systems

Who this book is for

If you are a data scientist, machine learning developer, or a deep learning enthusiast who wants to implement deep learning models in Java, this book is for you.

A basic understanding of Java programming, as well as some experience with machine learning and neural networks, is required to get the most out of this book.

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Learning Deep Learning: Theory and Practice of Neural Networks, Computer Vision, Natural Language Processing, and Transformers Using TensorFlow

Deep learning (DL) is a key component of today’s exciting advances in machine learning and artificial intelligence. Learning Deep Learning is a complete guide to DL. Illuminating both the core concepts and the hands-on programming techniques needed to succeed, this book is ideal for developers, data scientists, analysts, and others–including those with no prior machine learning or statistics experience.

After introducing the essential building blocks of deep neural networks, such as artificial neurons and fully connected, convolutional, and recurrent layers, Magnus Ekman shows how to use them to build advanced architectures, including the Transformer. He describes how these concepts are used to build modern networks for computer vision and natural language processing (NLP), including Mask R-CNN, GPT, and BERT. And he explains how a natural language translator and a system generating natural language descriptions of images.

Throughout, Ekman provides concise, well-annotated code examples using TensorFlow with Keras. Corresponding PyTorch examples are provided online, and the book thereby covers the two dominating Python libraries for DL used in industry and academia. He concludes with an introduction to neural architecture search (NAS), exploring important ethical issues and providing resources for further learning.

• Explore and master core concepts: perceptrons, gradient-based learning, sigmoid neurons, and back propagation
• See how DL frameworks make it easier to develop more complicated and useful neural networks
• Discover how convolutional neural networks (CNNs) revolutionize image classification and analysis
• Apply recurrent neural networks (RNNs) and long short-term memory (LSTM) to text and other variable-length sequences
• Master NLP with sequence-to-sequence networks and the Transformer architecture
• Build applications for natural language translation and image captioning

Learning Deep Learning: Theory and Practice of Neural Networks, Computer Vision, Natural Language Processing, and Transformers Using TensorFlow

Deep learning (DL) is a key component of today’s exciting advances in machine learning and artificial intelligence. Learning Deep Learning is a complete guide to DL.

Illuminating both the core concepts and the hands-on programming techniques needed to succeed, this book is ideal for developers, data scientists, analysts, and others–including those with no prior machine learning Books 2022 or statistics experience.

After introducing the essential building blocks of deep neural networks, such as artificial neurons and fully connected, convolutional, and recurrent layers, Magnus Ekman shows how to use them to build advanced architectures, including the Transformer.

He describes how these concepts are used to build modern networks for computer vision and natural language processing (NLP), including Mask R-CNN, GPT, and BERT. And he explains how a natural language translator and a system generating natural language descriptions of images.

Throughout, Ekman provides concise, well-annotated code examples using TensorFlow with Keras. Corresponding PyTorch examples are provided online, and the book thereby covers the two dominating Python libraries for DL used in industry and academia.

He concludes with an introduction to neural architecture search (NAS), exploring important ethical issues and providing resources for further learning.

• Explore and master core concepts: perceptrons, gradient-based learning, sigmoid neurons, and back propagation
• See how DL frameworks make it easier to develop more complicated and useful neural networks
• Discover how convolutional neural networks (CNNs) revolutionize image classification and analysis
• Apply recurrent neural networks (RNNs) and long short-term memory (LSTM) to text and other variable-length sequences
• Master NLP with sequence-to-sequence networks and the Transformer architecture
• Build applications for natural language translation and image captioning

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Graph Neural Networks: Foundations, Frontiers, and Applications

Deep Learning models are at the core of artificial intelligence research today. It is well known that deep learning techniques are disruptive for Euclidean data, such as images or sequence data, and not immediately applicable to graph-structured data such as text.

This gap has driven a wave of research for deep learning on graphs, including graph representation learning, graph generation, and graph classification.

The new neural network architectures on graph-structured data (graph neural networks, GNNs in short) have performed remarkably on these tasks, demonstrated by applications in social networks, bioinformatics, and medical informatics. 

Despite these successes, GNNs still face many challenges ranging from the foundational methodologies to the theoretical understandings of the power of the graph representation learning.

This book provides a comprehensive introduction of GNNs. It first discusses the goals of graph representation learning and then reviews the history, current developments, and future directions of GNNs.

The second part presents and reviews fundamental methods and theories concerning GNNs while the third part describes various frontiers that are built on the GNNs. The book concludes with an overview of recent developments in a number of applications using GNNs.

This book is suitable for a wide audience including undergraduate and graduate students, postdoctoral researchers, professors and lecturers, as well as industrial and government practitioners who are new to this area or who already have some basic background but want to learn more about advanced and promising techniques and applications.

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Efficient Processing of Deep Neural Networks

This book provides a structured treatment of the key principles and techniques for enabling efficient processing of deep neural networks (DNNs).

DNNs are currently widely used for many artificial intelligence (AI) applications, including Best computer vision Books, speech recognition, and robotics.

While DNNs deliver state-of-the-art accuracy on many AI tasks, it comes at the cost of high computational complexity.

Therefore, techniques that enable efficient processing of deep neural networks to improve metrics-such as energy-efficiency, throughput, and latency-without sacrificing accuracy or increasing hardware costs are critical to enabling the wide deployment of DNNs in AI systems.

The book includes background on DNN processing; a description and taxonomy of hardware architectural approaches for designing DNN accelerators; key metrics for evaluating and comparing different designs; features of the DNN processing that are amenable to hardware/algorithm co-design to improve energy efficiency and throughput; and opportunities for applying new technologies.

Readers will find a structured introduction to the field as well as a formalization and organization of key concepts from contemporary works that provides insights that may spark new ideas.

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Guide to Convolutional Neural Networks for Computer Vision

Computer vision has become increasingly important and effective in recent years due to its wide-ranging applications in areas as diverse as smart surveillance and monitoring, health and medicine, sports and recreation, robotics, drones, and self-driving cars.

Visual recognition tasks, such as image classification, localization, and detection, are the core building blocks of many of these applications, and recent developments in Convolutional Neural Networks (CNNs) have led to outstanding performance in these state-of-the-art visual recognition tasks and systems.

As a result, CNNs now form the crux of deep learning algorithms in computer vision.

This self-contained guide will benefit those who seek to both understand the theory behind CNNs and to gain hands-on experience on the application of CNNs in computer vision.

It provides a comprehensive introduction to CNNs starting with the essential concepts behind neural networks: training, regularization, and optimization of CNNs.

The book also discusses a wide range of loss functions, network layers, and popular CNN architectures, reviews the different techniques for the evaluation of CNNs, and presents some popular CNN tools and libraries that are commonly used in computer vision.

Further, this text describes and discusses case studies that are related to the application of CNN in computer vision, including image classification, object detection, semantic segmentation, scene understanding, and image generation.

This book is ideal for undergraduate and graduate students, as no prior background knowledge in the field is required to follow the material, as well as new researchers, developers, engineers, and practitioners who are interested in gaining a quick understanding of CNN models.

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Conclusion:

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