Best Books for Learning Artificial Neural Networks in 2021
A neural network is a powerful computational data model that is able to capture and represent complex input/output relationships. The motivation for the development of neural network technology stemmed from the desire to develop an artificial system that could perform "intelligent" tasks similar to those performed by the human brain. For deep learning artificial neural networks in 2021, we have listed some good books review that helps you to learn from beginner to master level.
Meet RoboChef, a Neural Network artificial intelligence that is trained to create recipes. RoboChef knows tens of thousands of recipes and produces groundbreaking culinary concoctions that are sure to surprise and amuse any chef.
Take a trip inside the mind of artificial intelligence to see what a computer thinks makes a good recipe. With instant classic dishes like "Homemade Crustless Baked Beans" and "Slow Cooker Egg Nachos," this cookbook is like nothing you've seen before. Intended as a humorous and educational look at what machine learning is capable of, RoboRecipes is the product of three different Neural Networks. The sometimes beautiful, sometimes grotesque images for each recipe were also generated and processed by artificial intelligence.
Would you like to be able to enhance your Python skills and have a thorough understanding of Neural Networks, Artificial Intelligence, and Data Science, even if you don’t know much (or nothing at all) about it? Here you will learn :
- What Machine Learning is, and What are its Concepts & Terms
- How to Work With Python for Data Science
- What are the Best, Essential Libraries for Machine Learning in Python
- Practical Codes and Exercises to Use Python
- What is the Tensorflow Library, and How it Works
- The Topnotch Data Mining Techniques in Data Science
This book takes you on a journey into Deep Learning and Neural Networks with important concepts and libraries like:•Convolutional and Recurrent Neural Networks•TensorFlow•Keras•PyTorch•Keras•Apache MXNet•Microsoft Cognitive Toolkit (CNTK)The final part of the book covers all foundational concepts that are required for Amazon Web Services (AWS) Certified Machine Learning Specialization by explaining how to deploy your models at scale on Cloud technologies. While AWS is used in the book for illustrative purposes, Microsoft Azure and Google Cloud are also introduced as alternative cloud technologies. After reading this book you will be able to:•Code in Python with confidence•Build new machine learning and deep learning models from scratch•Know how to clean and prepare your data for analytics•Speak confidently about statistical analysis techniques data Science was ranked the fast-growing field by LinkedIn and Data Scientist is one of the most highly sought after and lucrative careers in the world!
Apply computer vision and machine learning concepts in developing business and industrial applications using a practical, step-by-step approach. The book comprises four main sections starting with setting up your programming environment and configuring your computer with all the prerequisites to run the code examples.
What You Will Learn
- Employ image processing, manipulation, and feature extraction techniques
- Work with various deep learning algorithms for computer vision
- Train, manage, and tune hyperparameters of CNNs and object detection models, such as R-CNN, SSD, and YOLO
- Build neural network models using Keras and TensorFlow
- Discover best practices when implementing computer vision applications in business and industry
- Train distributed models on GPU-based cloud infrastructure
A step-by-step gentle journey through the mathematics of neural networks, and making your own using the Python computer language. Neural networks are a key element of deep learning and artificial intelligence, which today is capable of some truly impressive feats. Yet few really understand how neural network actually works. This guide will take you through 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.
What You Will Learn
- Introduce the mathematical ideas underlying the neural networks
- You'll learn to code in Python and make your own neural network
- Teaching it to recognize human handwritten numbers
- Gradually builds up a neural network to recognize human handwritten numbers
- Take a privileged peek inside the mysterious mind of a neural network.
This book, by the authors of the Neural Network Toolbox for MATLAB, provides a clear and detailed coverage of fundamental neural network architectures and learning rules. In it, the authors emphasize upon a coherent presentation of the principal neural networks, methods for training them and their applications to practical problems. Features Extensive coverage of training methods for both feedforward networks (including multilayer and radial basis networks) and recurrent networks.
What You Will Learn
- Including feature maps and learning vector quantization
- Tips for function approximation, pattern recognition
- Clustering and prediction
- In addition to conjugate gradient and Levenberg-Marquardt variations of the backpropagation algorithm
- Detailed examples and numerous solved problems.
From smart bulbs to self-driving cars, intelligent machines are becoming ever more prevalent in our day to day lives. The underpinning of this technology is called machine learning and is the same basic concept that is used by marketing experts to target ads on web pages and collect data about their customers. The uses of machine learning in today’s world are vast and ever expanding. The technology is poised to revolutionize the way people interact with machines on a daily basis. Understanding just how these programs and processes function can help you to navigate this new technology.
What You Will Learn
- Just what machine learning is and why it’s important
- Supervised versus unsupervised algorithms and the potential uses of each
- Description of some of the most popular machine learning algorithms
- The role of machine learning in programs like Cortana, Alexa, Siri, or Google assist.
- Explain current neural network technologies
- ncluding ReLU activation
- Stochastic gradient descent
- Cross-entropy, regularization
- Dropout, and visualization.
Networks are involved in many aspects of everyday life, from food webs in ecology and the spread of pandemics to social networking and public transportation. In fact, some of the most important and familiar natural systems and social phenomena are based on a networked structure. It is impossible to understand the spread of an epidemic, a computer virus, large-scale blackouts, or massive extinctions without taking account into the network structure that underlies all these phenomena.
What You Will Learn
- Guido Caldarelli and Michele Catanzaro discuss the nature and variety of networks
- Using everyday examples from society, technology, nature, and history to illuminate the science of network theory
- Describe the ubiquitous role of networks
- Discuss how networks can spontaneously collapse.
- Highlighting findings of complex network theory
- Important applications in genetics, ecology, communications, economics, and sociology.
A step-by-step visual journey through the mathematics of neural networks, and making your own using Python and Tensorflow. Once we dig a bit deeper though, we discover that a handful of mathematical functions play a major role in the trial and error process. It also becomes clear that a grasp of the underlying mathematics helps clarify how a network learns. You will learn to build a simple neural network using all the concepts and functions we learned in the previous few chapters. Our example will be basic but hopefully very intuitive. Many examples are available in online either hopelessly abstract or make use of the same data sets, which can be repetitive. Our goal is to be crystal clear and engaging, but with a touch of fun and uniqueness.
What you will gain from this book
- A deep understanding of how a Neural Network works
- How to build a Neural Network from scratch using Python
- Forward Propagation
- Calculating The Total Error
- Calculating The Gradients
- Updating The Weights.
Deep learning has been a great part of various scientific fields and you have already known the great significance of deep learning in comparison to traditional methods. At this point, you are also familiar with types of neural networks and their wide range of applications including image and speech recognition, natural language processing, video game development and other. Deep learning is pretty complex subject, but since you already have a fundamental knowledge of this topic, getting to know convolutional neural networks better is the next logical step.
What you will learn
- The architecture of convolutional neural networks
- Solving computer vision tasks using convolutional neural networks
- Python and computer vision
- Automatic image and speech recognition
- Theano and TenroeFlow image recognition
- Use MNIST vision dataset
- Commonly used convolutional filters.
The best part about this book is that it doesn’t require a college degree. Your high school math skills are quite enough for you to get a good grasp of the basics and learn how to build an artificial neural network. From non-mathematical explanations to teaching you the basic math behind the ANNs and training you how to actually program one, this book is the most helpful guide you will ever find. Carefully designed for you, the beginner, this guide will help you become a proud owner of a neural network in no time.
What You Will Learn
- The 6 unique benefits of neural networks
- The difference between biological and artificial neural networks
- Inside look into ANN (Artificial Neural Networks)
- How to teach neural networks to perform specific commands
- The different types of learning modalities (e.g. Hebbian Learning, unsupervised learning, supervised learning etc.)
- The architecture of ANN
- Basic math behind artificial neurons
- Simple networks for pattern classification
- The Hebb Rule
- Building a simple neural network code
- The backpropagation algorithm and how to program it
Neural networks is a book that provides a solid basis for simple neural network concepts. It incorporates a top-level view of neural network architectures for practical information analysis done with the aid of extensive step-by-step analysis on linear networks, in addition to, multi-layer perceptron for nonlinear prediction and classification enlightening all stages of processing and model development explained through useful examples and case studies. It is ideal for both beginners and advanced readers alike.
What You Will Learn
- An in-depth coverage on Self-organization for nonlinear information clustering
- Recurrent networks for linear-nonlinear time series forecasting
- Different community kinds appropriate for scientific record analysis
- Making your own neural network
- Teaching it to recognize human handwritten numbers, and performing as well as professionally developed networks.
It is a short book that will not take you a year to read. In the print version, this book is 124 pages. It explains neural networks, the core component of today’s AI systems, in enough detail that you will be able to get a basic understanding, even of technical discussions and introductory research papers. You will be able to not only read about AI in magazines and news outlets with a new and deep understanding but also talk about it with the experts in the field and understand lectures on the topic. This book will empower you to understand this new world and to make your own decisions.