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10 Best Deep Learning Books for Beginner & Experts
Table of Contents
Are you looking for deep learning sources? If yes Here we have listed the best deep learning books.
What is Deep learning?
Deep Learning is a subset of Artificial Intelligence, which directs a computer to perform classification tasks directly from texts, images, or sounds. Deep Learning is also a specialized form of Machine Learning. It is one of the most popular domains in the AI space, allowing you to develop multi-layered models of varying complexities. The term deep refers to the number of hidden layers in the network. For optimal results, Deep Learning requires large amounts of data and substantial computing power.
Most methods of Deep Learning are on neural network architectures; hence, it is sometimes referred to as Deep Neural Networks as well. Deep Learning has its applications in the fields of Automated Driving, Image Recognition, News Aggregation, and Fraud Detection, Natural Language Processing, Virtual Assistants, Media and Entertainment, Healthcare, Security, Personalized Services, and more.
Best Deep Learning Books
Understanding Deep Learning is easy if you have a concept of Machine Learning. Having a good knowledge of Linear Algebra, Calculus, Probability, Programming Language, Statistics is an added advantage. We have prepared a list of books that you can refer to as understanding Deep Learning. This list covers core Deep Learning Books and those relevant to complement your field of expertise.
1. Deep Learning (Adaptive Computation and Machine Learning series) by Ian Goodfellow, Yoshua Bengio, Aaron Courville, Francis Bach
This book introduces a broad range of topics in deep learning. The text offers mathematical and conceptual background, covering relevant concepts in linear algebra, probability theory and information theory, numerical computation, and machine learning. It describes deep learning techniques used by practitioners in industry, including deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical methodology.
It surveys applications such as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames.
Finally, the book offers research perspectives, covering such theoretical topics as linear factor models, autoencoders, representation learning, structured probabilistic models, Monte Carlo methods, the partition function, approximate inference, and deep generative models. Deep Learning can be used by undergraduate or graduate students planning careers in either industry or research, and by software engineers who want to begin using deep learning in their products or platforms.
2. Deep Learning for Natural Language Processing: Applications of Deep Neural Networks to Machine Learning Tasks by Pearson Learn IT
Advance your career with self-paced online video courses and Learn anywhere, anytime, on any device. Pearson Learn IT courses can teach a lot in a little amount of time, and material is easy to absorb and remember. It is an intuitive introduction to processing natural language data with Deep Learning models Deep Learning for Natural Language Processing. Demonstrates concepts with real use cases and step-by-step, easy to follow exercises — video-based training by leading experts with years of experience in Industry, Academia, or both.
It is intended for beginners and intermediate programmers. It extensively covers the implementation of a convolutional neural network. It is structured around a series of practical code examples, which helps to illustrate each new concept and demonstrate the best practices. It is a good book for deep learning using Keras. By the end of this book, you have become a Keras expert and can apply deep learning in your projects.
- Practical code examples
- An in-depth introduction to Keras
- Teaches the difference between Deep Learning and AI
Advanced Deep Learning with Keras is a comprehensive guide to the advanced deep learning techniques available today so that you can create your own cutting-edge AI. Using Keras is an open-source deep-learning library, the book gives you hands-on projects that show you how to create more effective AI with the latest techniques. It gives an overview of MLPs, CNNs, and RNNs, which are the building blocks for the more advanced techniques in the book.
This book explains how to implement deep learning models with Keras and Tensorflow and moves forward to advanced techniques as you explore deep neural network architectures, including ResNet and DenseNet, and how to create Autoencoders. You then learn all about Generative Adversarial Networks (GANs), and how they can open new levels of AI performance. Implements variational AutoEncoders (VAEs), and you'll see how GANs and VAEs have the generative power to synthesize data that can be extremely convincing to humans.
Finally, you'll learn how to implement Deep Reinforcement Learning (DRL) such as Deep Q-Learning and Policy Gradient Methods, which are critical to many recent results in AI. Prior knowledge of Keras or TensorFlow though not required but would be helpful.
Understand basic to advanced deep learning algorithms, the mathematical principles behind them, and their practical applications. This book introduces you to access deep learning algorithms-from essential to advanced-and shows you how to implement them from scratch using TensorFlow. Throughout the book, you gain insights into each algorithm, the mathematical principles behind it, and how to implement it in the best possible manner.
The book starts by explaining how you can build your neural networks, followed by introducing you to TensorFlow, the powerful Python-based library for machine learning and deep learning. Moving on, you get up to speed with gradient descent variants, such as NAG, AMSGrad, AdaDelta, Adam, and Nadam. The book then provides you with insights into RNNs and LSTM and how to generate song lyrics with RNN. Next, you master math for convolutional and capsule networks, widely used for image recognition tasks. Then you learn how machines understand the semantics of words and documents using CBOW, skip-gram, and PV-DM. Afterwards, you explore various GANs, including InfoGAN and LSGAN, and autoencoders, such as contractive autoencoders and VAE.
By the end of this book, you equip yourself with all the skills you need to implement deep learning in your projects. If you are a machine learning engineer, data scientist, AI developer, or want to focus on neural networks and deep learning, this book is for you. Those who are entirely new to deep learning, but have some experience in machine learning and Python programming find the book very helpful.
- Get up-to-speed with building your neural networks from scratch.
- Gain insights into the mathematical principles behind deep learning algorithms.
- Implement popular deep learning algorithms such as CNNs, RNNs, and more using TensorFlow.
This book shows how to use simple, efficient tools to implement programs to learn data. It uses Scikit and Tensorflow to give an intuitive understanding of the concepts and tools for building intelligent systems. Throughout this book, you learn a range of techniques, starting with simple linear regression and progressing to deep neural networks. With exercises in each chapter to help you apply what youíve learned. Make sure you have a programming base to get started on it.
Further, this book allows you to:
- Explore the machine learning landscape, particularly neural nets.
- Use scikit-learn to track an example machine-learning project end-to-end.
- Explore several training models, including support vector machines, decision trees, random forests, and ensemble methods.
- Use the TensorFlow library to build and train neural nets.
- Dive into neural net architectures, including convolutional nets, recurrent nets, and deep reinforcement learning.
- Learn techniques for training and scaling deep neural nets.
- Apply practical code examples without acquiring excessive machine learning theory or algorithm details.
7. Machine Intelligence: Demystifying Machine Learning, Neural Networks and Deep Learning by Suresh Samudrala
This book explains the core concepts of machine learning algorithms using illustrations, data tables, and examples. It further covers classical machine learning, neural networks, and deep learning algorithms. It has a simple approach that builds up from basics, which would help software engineers and students looking to learn about the field as well as those who might have started without the benefit of a structured introduction or sound basics.
The book has depth yet avoids excessive mathematics. The coverage of the subject is excellent and has most of the concepts required for understanding machine learning if someone is looking for depth. This book is intended for IT and business professionals looking to gain proficiency in these technologies but is turned off by complex mathematical equations.
This book is also useful for students in the area of artificial intelligence and machine learning to gain a conceptual understanding of the algorithms and get an industry perspective. For senior management, it provides a good overview.
This book covers both classical and modern models in deep learning. The primary focus is on the theory and algorithms of deep learning. The book is also rich in discussing different applications 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 chapters of this book span three categories:
- The Basics of Neural Networks
- Fundamentals of Neural Networks
- Advanced topics in Neural Networks
The book is 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 to provide an understanding of the practical uses of each class of techniques.
It is the first comprehensive treatment of feedforward neural networks from the perspective of statistical pattern recognition. After introducing the basic concepts, the book examines techniques for modeling probability density functions and the properties and merits of the multilayer perceptron and radial basis function network models.
Also covered are various forms of error functions, ranking algorithms for error function minimization, learning and generalization in neural networks, and Bayesian techniques and their applications. Designed as a text, with over 100 exercises, this fully up-to-date work benefit anyone involved in the fields of neural computation and pattern recognition.
10. Neural Smithing: Supervised Learning in Feedforward Artificial Neural Networks by Russell Reed, Robert J MarksII
This book focuses on the subset of feedforward artificial neural networks called multilayer perceptrons (MLP). These are the most widely used neural networks, with applications as diverse as finance (forecasting), manufacturing (process control), and science (speech and image recognition).
This book presents an extensive and practical overview of almost every aspect of MLP methodology, progressing from an initial discussion of what MLPs are and how they might be used to an in-depth examination of technical factors affecting performance. The book can be used as a tool kit by readers interested in applying networks to specific problems. Yet, it also presents theory and references outlining the last ten years of MLP research.
Bonus Deep Learning Books
This book is a one-stop guide to implementing award-winning, and cutting-edge CNN architectures. This book starts with an overview of deep neural networks with the example of image classification and walks you through building your first CNN for human face detectors. You learn to use concepts like transfer learning with CNN, and Auto-Encoders to build compelling models, even when not much of supervised training data of labeled images are available. Later this book builds upon building advanced vision-related algorithms for object detection, instance segmentation, generative adversarial networks, image captioning, attention mechanisms for vision, and recurrent models for vision.
By the end of this book, you should be ready to implement advanced, effective, and efficient CNN models at your professional project or personal initiatives by working on intricate images and video datasets. Convolutional Neural Network (CNN) is revolutionizing several application domains such as visual recognition systems, self-driving cars, medical discoveries, innovative eCommerce, and more. You learn to create innovative solutions around image and video analytics to solve complex machine learning and computer vision-related problems and implement real-life CNN models.
This book is for data scientists, machine learning and deep learning practitioners, Cognitive and Artificial Intelligence enthusiasts who want to move one step further in building Convolutional Neural Networks. Get hands-on experience with extreme datasets and different CNN architectures to build efficient and smart ConvNet models. Basic knowledge of deep learning concepts and Python programming language is expected.
- Fast-paced guide with use cases and real-world examples to get well versed with CNN techniques
- Implement CNN models on image classification, transfer learning, Object Detection, Instance Segmentation, GANs, and more.
- Implement compelling use-cases like image captioning, reinforcement learning for hard attention, and recurrent attention models.
Deep Learning has now attracted much attention and continues to do so as it has a very high potential for real-world applications. It enables us to leverage all of the labelled, unlabelled, structured, and unstructured data to its full extent and provides enormous benefits for real-world applications. It is a powerful engine for producing actionable results. Deep Learning is here to stay for a while, and a career in this field helps you harvest substantial benefits both personally and professionally in the long run.
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