Top 10 books on Deep Learning
In this post, you will discover the top 10 books available right now on deep learning. Currently, there aren’t many books at the moment in deep learning because it’s still an area of research. There are quite a few available online in which you may purchase.
The book Deep Learning with Python written by Keras creator and Google AI researcher François Chollet introduces the field of Deep Learning using python with the powerful and Keras library. It was written in order to build the knowledge and minds of individuals using intuitive explanations and practical examples. The purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.
2. Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems
Through a series of recent breakthroughs, Deep Learning has boosted the entire field of machine learning. Now, even programmers who know close to nothing about this technology can use simple, efficient tools to implement programs capable of learning from data. This practical book shows you how.
By using concrete examples, minimal theory, and two production-ready Python frameworks—scikit-learn and TensorFlow—author Aurélien Géron helps you gain an intuitive understanding of the concepts and tools for building intelligent systems. You’ll 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 you’ve learned, all you need is programming experience to get started.
Although interest in machine learning has reached a high point, lofty expectations often scuttle projects before they get very far. How can machine learning—especially deep neural networks—make a real difference in your organization? This hands-on guide not only provides the most practical information available on the subject but also helps you get started building efficient deep learning networks.
Authors Adam Gibson and Josh Patterson provide theory on deep learning before introducing their open-source Deeplearning4j (DL4J) library for developing production-class workflows. Through real-world examples, you’ll learn methods and strategies for training deep network architectures and running deep learning workflows on Spark and Hadoop with DL4J.
The book Deep Learning for Computer Vision with Python provides beginners with an easy to follow theory. Also accompanied with fascinating examples in Python (Keras library). With an easy to follow book, it provides each reader with a detailed code along with comments from building shallow neural networks, up to more advanced deep learning architectures. Since this book is more computer vision related all the datasets used as examples are images: cats vs.dogs, MNIST (Handwritten digit dataset), CIFAR, smile recognition database, and others.
5. Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems
Through a series of recent breakthroughs, deep learning has boosted the entire field of machine learning. Now, even programmers who know close to nothing about this technology can use simple, efficient tools to implement programs capable of learning from data. This practical book shows you how.
By using concrete examples, minimal theory, and two production-ready Python frameworks—Scikit-Learn and TensorFlow—author Aurélien Géron helps you gain an intuitive understanding of the concepts and tools for building intelligent systems. You’ll learn a range of techniques, starting with simple linear regression and progressing to deep neural networks.
Deep Learning (Adaptive Computation and Machine Learning series) introduces a broad range of topics in deep learning. Covering mathematical and conceptual background with techniques both applied industry and research perspectives. This book with no doubt was written by leaders in the area of deep learning providing the reader with a depth overview of the topic. Since it’s too in-depth it relies mostly on advanced mathematical concepts that could be challenging for beginners. So if you are just starting out, we recommend taking up a book that gently explains deep learning.
“Written by three experts in the field, Deep Learning is the only comprehensive book on the subject.”
―Elon Musk, cochair of OpenAI; cofounder and CEO of Tesla and SpaceX
Deep learning has the reputation as an exclusive domain for math PhDs. Not so. With this book, programmers comfortable with Python will learn how to get started with deep learning right away.
Using PyTorch and the fastai deep learning library, you’ll learn how to train a model to accomplish a wide range of tasks—including computer vision, natural language processing, tabular data, and generative networks. At the same time, you’ll dig progressively into deep learning theory so that by the end of the book you’ll have a complete understanding of the math behind the library’s functions.
Generative modeling is one of the hottest topics in AI. It’s now possible to teach a machine to excel at human endeavors such as painting, writing, and composing music. With this practical book, machine-learning engineers and data scientists will discover how to re-create some of the most impressive examples of generative deep learning models, such as variational autoencoders,generative adversarial networks (GANs), encoder-decoder models, and world models.
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.
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.
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