Leverage the power of deep learning and Keras to develop smarter and more efficient data models Key Features Understand different neural networks and their implementation using Keras Explore recipes for training and fine-tuning your neural network models Put your deep learning knowledge to practice with real-world use-cases, tips, and tricks Book DescriptionKeras has quickly emerged as a popular deep learning library. Written in Python, it allows you to train convolutional as well as recurrent neural networks with speed and accuracy. The Keras Deep Learning Cookbook shows you how to tackle different problems encountered while training efficient deep learning models, with the help of the popular Keras library. Starting with installing and setting up Keras, the book demonstrates how you can perform deep learning with Keras in the TensorFlow. From loading data to fitting and evaluating your model for optimal performance, you will work through a step-by-step process to tackle every possible problem faced while training deep models. You will implement convolutional and recurrent neural networks, adversarial networks, and more with the help of this handy guide. In addition to this, you will learn how to train these models for real-world image and language processing tasks. By the end of this book, you will have a practical, hands-on understanding of how you can leverage the power of Python and Keras to perform effective deep learning What you will learn Install and configure Keras in TensorFlow Master neural network programming using the Keras library Understand the different Keras layers Use Keras to implement simple feed-forward neural networks, CNNs and RNNs Work with various datasets and models used for image and text classification Develop text summarization and reinforcement learning models using Keras Who this book is forKeras Deep Learning Cookbook is for you if you are a data scientist or machine learning expert who wants to find practical solutions to common problems encountered while training deep learning models. A basic understanding of Python and some experience in machine learning and neural networks is required for this book.

Neural Networks and their implementation decoded with TensorFlow About This Book * Develop a strong background in neural network programming from scratch, using the popular Tensorflow library. * Use Tensorflow to implement different kinds of neural networks - from simple feedforward neural networks to multilayered perceptrons, CNNs, RNNs and more. * A highly practical guide including real-world datasets and use-cases to simplify your understanding of neural networks and their implementation. Who This Book Is For This book is meant for developers with a statistical background who want to work with neural networks. Though we will be using TensorFlow as the underlying library for neural networks, book can be used as a generic resource to bridge the gap between the math and the implementation of deep learning. If you have some understanding of Tensorflow and Python and want to learn what happens at a level lower than the plain API syntax, this book is for you. What You Will Learn * Learn Linear Algebra and mathematics behind neural network. * Dive deep into Neural networks from the basic to advanced concepts like CNN, RNN Deep Belief Networks, Deep Feedforward Networks. * Explore Optimization techniques for solving problems like Local minima, Global minima, Saddle points * Learn through real world examples like Sentiment Analysis. * Train different types of generative models and explore autoencoders. * Explore TensorFlow as an example of deep learning implementation. In Detail If you're aware of the buzz surrounding the terms such as "machine learning," "artificial intelligence," or "deep learning," you might know what neural networks are. Ever wondered how they help in solving complex computational problem efficiently, or how to train efficient neural networks? This book will teach you just that. You will start by getting a quick overview of the popular TensorFlow library and how it is used to train different neural networks. You will get a thorough understanding of the fundamentals and basic math for neural networks and why TensorFlow is a popular choice Then, you will proceed to implement a simple feed forward neural network. Next you will master optimization techniques and algorithms for neural networks using TensorFlow. Further, you will learn to implement some more complex types of neural networks such as convolutional neural networks, recurrent neural networks, and Deep Belief Networks. In the course of the book, you will be working on real-world datasets to get a hands-on understanding of neural network programming. You will also get to train generative models and will learn the applications of autoencoders. By the end of this book, you will have a fair understanding of how you can leverage the power of TensorFlow to train neural networks of varying complexities, without any hassle. While you are learning about various neural network implementations you will learn the underlying mathematics and linear algebra and how they map to the appropriate TensorFlow constructs. Style and Approach This book is designed to give you just the right number of concepts to back up the examples. With real-world use cases and problems solved, this book is a handy guide for you. Each concept is backed by a generic and real-world problem, followed by a variation, making you independent and able to solve any problem with neural networks. All of the content is demystified by a simple and straightforward approach.

Strategically design, troubleshoot, and automate Docker containers from development to deployment About This Book * Utilize current and emergent technologies for effective Docker orchestration and management * A step-by-step guide to diagnosing and fixing problems with Docker containers. Who This Book Is For This book is intended for seasoned solutions architects, developers, and programmers, system engineers, and administrators to help you troubleshoot common areas of Docker containerization. If you are looking to build production-ready Docker containers for automated deployment, you will be able to master and troubleshoot both the basic functions and the advanced features of Docker. Advanced familiarity with the Linux command line syntax, unit testing, the Docker Registry, Github, and leading container hosting platforms and Cloud Service Providers (CSP) are the prerequisites. What You Will Learn * Install Docker ecosystem tools and services, Microservices and N-tier applications * Create re-usable, portable containers with help of automation tools * Network and inter-link containers * Attach volumes securely to containers * Consume and troubleshoot Docker APIs * Troubleshooting issue of Docker deployment in Public cloud * Ease the process of container management with Kubernetes In Detail This book will traverse some common best practices to for complex application scenarios where troubleshooting can be successfully employed to provide the repeatable processes and advantages that containers can deliver. This book will be a practical guide showing how to fix real-life issues related to installation, memory, Dockerfile syntax, connection, authorization, networking and so on in Docker. This book will also teach how to solve errors that occur during advanced setup and administration and deployment in a step-by-step fashion. By sequentially working through the real-world production scenarios in each chapter throughout the book, you will gain insight into and mastery of common areas not only for effective troubleshooting, but ways and means to avoid troubleshooting in the first place. This book will also cover tips and tricks that make the workflow easier. Style and approach An easy-to-follow guide full of interactive examples of real-world development and deployment scenarios. Ample screenshots, workflows, complementary tools, and related terminal commands are provided to address a wide range of practical and situational applications.