Thursday, August 5, 2021

5 Deep Learning Books you should read

Applied deep learning

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The goal of this book is not to make you a Python or TensorFlow expert, or someone who can develop new complex models. Instead, it's an efficient way to learn the right techniques by covering more complex examples with real examples and with fully fledged and tested Python code that you can reuse. The book also covers data preparation and error analysis, from data preparation to error analysis. The goal of this book is to let you see more advanced material with new eyes. I cover the mathematical background as much as I can, because it is necessary for a complete understanding of the difficulties and reasoning behind many concepts.

Appreciating why a library such as TensorFlow makes your life easier is only possible if you try to develop a trivial model with one neuron from scratch. Once you have done it once, you will remember it forever, and you will really appreciate libraries such as TensorFlow. In each chapter, I highlight important tips to develop things efficiently in Python.

If you have a solid linear algebra background, I strongly advise you not to skip the mathematical parts of this book. You should understand and feel confident with such concepts as a sum or a mathematical series. If you feel unsure about these, review them before starting the book; otherwise, you will miss some important concepts that you must have a firm Introduction xx grasp on to proceed in your deep-learning career. In this book, you will learn how to set up your Python environment and what computational graphs are. In Chapter 1, we will look at some basic examples of mathematical calculations performed using TensorFlow.

We will then build our first real network with Tensor Flow and start looking at more complex variations of gradient descent algorithms. In Chapter 7, we look at the black box class of problems and what hyperparameter tuning is. We will look at such algorithms as grid and random search and at which is more efficient and why. Then, we will talk about some tricks, s that can be used to improve your results.

Java deep learning essentials

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This book is for Java developers who want to know about deep learning algorithms and wish to implement them in applications. It covers the core concepts of and approaches to both machine learning and deep learning. No previous experience in machine learning is required, so elementary Java developers will find this book useful for developing both their Java skills and their deep learning skills.

In this book, you'll learn how deep learning has evolved and how to use it in the real world. You'll find out more about dropout and convolutional neural networks, as well as dive into Java deep learning library DL4J. Theano, TensorFlow, and Caffe will help you devise strategies to use deep learning algorithms and libraries in your own life.

Deep larning with azure

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Mathew, Danielle, and Wee Hyong present a practical overview of why the impact of AI and deep learning has accelerated recently. They build on their experiences as leading data scientists at Microsoft working with external customers. Mathew: AI is about empowering people and organizations to reason and interact with the increasingly digital world around us Microsoft's AI platform is a collection of interoperating SaaS applications, which collect and organize all relevant data and interactions in the cloud. The Microsoft AI platform can be used to build next-generation systems of intelligence that understand, reasons, and interact in a very natural way. It's not just one or two components, or a few components from open source integrated with existing enterprise applications. Instead, you need a comprehensive collection of platform services that only a cloud platform can bring, including identity and security.

Microsoft provides services and infrastructure to enable others who want to build intelligent applications with the Microsoft AI platform. In Part I of the book, we introduce the basic concepts of AI and the role Microsoft has related to AI solutions. We outline example use cases using AI, especially employing deep learning techniques, which span several verticals such as manufacturing, health care, and utilities. And in Part II, we discuss limitations of deep learning and go over how to get started. In Part III, we cover three common types of deep learning models that are useful to understand in building custom AI solutions.

Each chapter includes links to code samples for understanding the type of network and how one can build such a network using the Microsoft AI platform. In the next two chapters, we'll look at some of the most commonly used examples of neural networks.

Deep learning 

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The first four chapters of this book are focused on enough theory and fundamentals to give you a working foundation for the rest of the book. The last five chapters work from these concepts to lead you through a series of practical paths in deep learning using DL4J. These include building deep networks, tuning techniques and running deep learning workflows on Spark. The book DL4J is a new book that explains how you can run your own Spark clusters on top of Hadoop. The book has many Appendix chapters for topics that were relevant yet didn't fit directly in the main chapters.

We feel that too many books leave out core topics that the enterprise practitioner often needs for a quick review. We chose to start the book with a series of fundamentals to take you on a full journey through deep learning. In the following sections, we suggest some reading strategies for different backgrounds. You can skip Chapters 1 and 2 and get right to the deep learning fundamentals.

O'Reilly is an independent, non-profit organization that provides free software to the public sector. O'Reilly offers a number of tools and resources to help you get your job done. You do not need to contact us for permission unless you're reproducing a significant portion of the code in your programs or documentation.

Deep Learning with Python

This book offers a practical, hands-on exploration of deep learning. It avoids mathematical notation, preferring to explain quantitative concepts via code snippets. You'll learn from more than 30 code examples that include detailed commentary, and simple high-level explanations of everything you need to know. The code examples use the Python deep-learning framework Keras, with Tensor-Flow as a backend engine.

This book is written for people with Python programming experience who want to get started with machine learning and deep learning. It can also be valuable to many different types of readers, from data scientists to the technically minded to those who don't code regularly. You don't need an advanced mathematics background, but high school–level mathematics should suffice in order to follow along.

All of this book's code examples use the Keras deep-learning framework, which is open source and free to download. You'll need access to a UNIX\nmachine; it's possible to use Windows, too, but I don't recommend it. I also recommend that you have a recent NVIDIA GPU on your machine, such as a\nTITAN X. This isn't required, but it will make your experience better by allowing you to run the code examples several times faster.

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