Keras Deep Learning Essentials
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Keras Deep Learning Essentials
"Keras Deep Learning Essentials" "Keras Deep Learning Essentials" is an authoritative guide that equips practitioners, researchers, and aspiring deep learning engineers with the essential knowledge and hands-on techniques for building, optimizing, and deploying state-of-the-art neural networks using the Keras framework. Beginning with the fundamental mathematical principles behind deep learning and a survey of modern neural architectures, the book offers clear explanations of Keras’s design philosophy, its seamless integration with TensorFlow, and the complete pipeline from initial prototyping to scalable production inference. With a strong emphasis on practical environment setup, the book ensures readers are well-prepared to harness advanced hardware acceleration and library dependencies for robust model development. Each chapter delves into a core aspect of the Keras workflow, from model construction patterns utilizing the Sequential and Functional APIs to sophisticated techniques such as subclassing, transfer learning, and custom layer engineering. Readers master the intricacies of efficient data pipelines, advanced feature engineering, and data augmentation strategies, supported by real-world guidance on handling class imbalance, online data validation, and complex input modalities. Model training and optimization at scale are addressed through modern loss and metric engineering, distributed and multi-GPU strategies, and advanced debugging and profiling to ensure performance and reliability for the most demanding applications. Beyond model development, "Keras Deep Learning Essentials" provides a comprehensive exploration of evaluation, explainability, and productionization. The book details best practices for model serialization, serving, mobile and edge deployment, and integration with MLOps pipelines, as well as crucial topics such as compliance, security, and sustainable AI. Advanced chapters discuss Keras’s role in cutting-edge research areas, including reinforcement learning, graph neural networks, and federated learning, positioning readers to innovate within both research and industry environments. This essential resource concludes with timely insights into emerging trends, reproducibility, and the evolving Keras ecosystem, making it indispensable for anyone seeking to advance in the deep learning domain.
R Deep Learning Essentials
Implement neural network models in R 3.5 using TensorFlow, Keras, and MXNet Key Features Use R 3.5 for building deep learning models for computer vision and text Apply deep learning techniques in cloud for large-scale processing Build, train, and optimize neural network models on a range of datasets Book Description Deep learning is a powerful subset of machine learning that is very successful in domains such as computer vision and natural language processing (NLP). This second edition of R Deep Learning Essentials will open the gates for you to enter the world of neural networks by building powerful deep learning models using the R ecosystem. This book will introduce you to the basic principles of deep learning and teach you to build a neural network model from scratch. As you make your way through the book, you will explore deep learning libraries, such as Keras, MXNet, and TensorFlow, and create interesting deep learning models for a variety of tasks and problems, including structured data, computer vision, text data, anomaly detection, and recommendation systems. You’ll cover advanced topics, such as generative adversarial networks (GANs), transfer learning, and large-scale deep learning in the cloud. In the concluding chapters, you will learn about the theoretical concepts of deep learning projects, such as model optimization, overfitting, and data augmentation, together with other advanced topics. By the end of this book, you will be fully prepared and able to implement deep learning concepts in your research work or projects. What you will learn Build shallow neural network prediction models Prevent models from overfitting the data to improve generalizability Explore techniques for finding the best hyperparameters for deep learning models Create NLP models using Keras and TensorFlow in R Use deep learning for computer vision tasks Implement deep learning tasks, such as NLP, recommendation systems, and autoencoders Who this book is for This second edition of R Deep Learning Essentials is for aspiring data scientists, data analysts, machine learning developers, and deep learning enthusiasts who are well versed in machine learning concepts and are looking to explore the deep learning paradigm using R. Fundamental understanding of the R language is necessary to get the most out of this book.
Deep Learning with TensorFlow 2 and Keras
Author: Antonio Gulli
language: en
Publisher: Packt Publishing Ltd
Release Date: 2019-12-27
Build machine and deep learning systems with the newly released TensorFlow 2 and Keras for the lab, production, and mobile devices Key FeaturesIntroduces and then uses TensorFlow 2 and Keras right from the startTeaches key machine and deep learning techniquesUnderstand the fundamentals of deep learning and machine learning through clear explanations and extensive code samplesBook Description Deep Learning with TensorFlow 2 and Keras, Second Edition teaches neural networks and deep learning techniques alongside TensorFlow (TF) and Keras. You’ll learn how to write deep learning applications in the most powerful, popular, and scalable machine learning stack available. TensorFlow is the machine learning library of choice for professional applications, while Keras offers a simple and powerful Python API for accessing TensorFlow. TensorFlow 2 provides full Keras integration, making advanced machine learning easier and more convenient than ever before. This book also introduces neural networks with TensorFlow, runs through the main applications (regression, ConvNets (CNNs), GANs, RNNs, NLP), covers two working example apps, and then dives into TF in production, TF mobile, and using TensorFlow with AutoML. What you will learnBuild machine learning and deep learning systems with TensorFlow 2 and the Keras APIUse Regression analysis, the most popular approach to machine learningUnderstand ConvNets (convolutional neural networks) and how they are essential for deep learning systems such as image classifiersUse GANs (generative adversarial networks) to create new data that fits with existing patternsDiscover RNNs (recurrent neural networks) that can process sequences of input intelligently, using one part of a sequence to correctly interpret anotherApply deep learning to natural human language and interpret natural language texts to produce an appropriate responseTrain your models on the cloud and put TF to work in real environmentsExplore how Google tools can automate simple ML workflows without the need for complex modelingWho this book is for This book is for Python developers and data scientists who want to build machine learning and deep learning systems with TensorFlow. This book gives you the theory and practice required to use Keras, TensorFlow 2, and AutoML to build machine learning systems. Some knowledge of machine learning is expected.