Python Deep Learning Cookbook. Over 75 practical recipes on neural network modeling, reinforcement learning, and transfer learning using Python

Indra den Bakker

Deep Learning is revolutionizing a wide range of industries. For many applications, deep learning has proven to outperform humans by making faster and more accurate predictions. This book provides a top-down and bottom-up approach to demonstrate deep learning solutions to real-world problems in different areas. These applications include Computer Vision, Natural Language Processing, Time Series, and Robotics. The Python Deep Learning Cookbook presents technical solutions to the issues presented, along with a detailed explanation of the solutions. Furthermore, a discussion on corresponding pros and cons of implementing the proposed solution using one of the popular frameworks like TensorFlow, PyTorch, Keras and CNTK is provided. The book includes recipes that are related to the basic concepts of neural networks. All techniques s, as well as classical networks topologies. The main purpose of this book is to provide Python programmers a detailed list of recipes to apply deep learning to common and not-so-common scenarios.

Infrastructure as Code (IAC) Cookbook. Automate complex infrastructures

Stephane Jourdan, Pierre Pomes

Para 1: Infrastructure as code is transforming the way we solve infrastructural challenges. This book will show you how to make managing servers in the cloud faster, easier and more effective than ever before. With over 90 practical recipes for success, make the very most out of IAC.

Getting Started with Nano Server. Automate multiple VMs and transform your datacenter

Charbel Nemnom

Nano Server allows developers and operations teams to work closely together and use containers that package applications so that the entire platform works as one. The aim of Nano Server is to help applications run the way they are intended to. It can be used to run and deploy infrastructures (acting as a compute host, storage host, container, or VM guest operating system) without consuming significant resources. Although Nano Server isn't intended to replace Server 2016 or 2012 R2, it will be an attractive choice for developers and IT teams.Want to improve your ability to deploy a new VM and install and deploy container apps within minutes? You have come to the right place! The objective of this book is to get you started with Nano Server successfully. The journey is quite exciting, since we are introducing you to a cutting-edge technology that will revolutionize today's datacenters. We'll cover everything from the basic to advanced topics. You'll discover a lot of added value from using Nano Server, such as hundreds of VM types on a single host through a small footprint, which could be a big plus for you and your company. After reading this book, you will have the necessary skills to start your journey effectively using Nano Server.

Hands-On Automation Testing with Java for Beginners. Build automation testing frameworks from scratch with Java

Rahul Shetty

Java is one of the most commonly-used software languages by programmers and developers. Are you from a non-technical background and looking to master Java for your automation needs? Then Hands-On Automation Testing with Java for Beginners is for you.This book provides you with efficient techniques to effectively handle Java-related automation projects. You will learn how to handle strings and their functions in Java. As you make your way through the book, you will get to grips with classes and objects, along with their uses. In the concluding chapters, you will learn about the importance of inheritance and exceptions with practical examples. By the end of this book, you will have gained comprehensive knowledge of Java.

Mastering Spark for Data Science. Lightning fast and scalable data science solutions

Andrew Morgan, Antoine Amend, Matthew Hallett, David George

Data science seeks to transform the world using data, and this is typically achievedthrough disrupting and changing real processes in real industries. In order to operate at this level you need to build data science solutions of substance –solutions that solve real problems. Spark has emerged as the big data platform of choice for data scientists due to its speed, scalability, and easy-to-use APIs.This book deep dives into using Spark to deliver production-grade data sciencesolutions. This process is demonstrated by exploring the construction of a sophisticated global news analysis service that uses Spark to generate continuous geopolitical and current affairs insights.You will learn all about the core Spark APIs and take a comprehensive tour of advanced libraries, including Spark SQL, Spark Streaming, MLlib, and more.You will be introduced to advanced techniques and methods that will help you to construct commercial-grade data products. Focusing on a sequence of tutorials that deliver a working news intelligence service, you will learn about advanced Spark architectures, how to work with geographic data in Spark, and how to tune Spark algorithms so they scale linearly.

Angular Router. From Angular core team member and creator of the router

Victor Savkin

Managing state transitions is one of the hardest parts of building applications. This is especially true on the web, where you also need to ensure that the state is reflected in the URL. In addition, you might want to split applications into multiple bundles and load them on demand. Doing this transparently isn’t easy. The Angular router solves these problems. Using the router, you can declaratively specify application states, manage state transitions while taking care of the URL, and load bundles on demand.This book is a complete description of the Angular router written by its designer. It goes far beyond a how-to-get-started guide and talks about the library in depth. The mental model, design constraints, and the subtleties of the API-everything is covered. You’ll learn in detail how to use the router in your own applications. Predominantly, you’ll understand the inner workings of the router and how you can configure it to work with any edge cases you come across in your sites.Throughout the book, you’ll see examples from real-world use in the MailApp application. You can view the full source of this application and see how the router code works to manage the state of the application and define what is visible on screen. Reading this book will give you deep insights into why the router works the way it does and will make you an Angular router expert.

Keras Deep Learning Cookbook. Over 30 recipes for implementing deep neural networks in Python

Rajdeep Dua, Manpreet Singh Ghotra

Keras 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

scikit-learn Cookbook. Over 80 recipes for machine learning in Python with scikit-learn - Second Edition

Julian Avila, Trent Hauck

Python is quickly becoming the go-to language for analysts and data scientists due to its simplicity and flexibility, and within the Python data space, scikit-learn is the unequivocal choice for machine learning. This book includes walk throughs and solutions to the common as well as the not-so-common problems in machine learning, and how scikit-learn can be leveraged to perform various machine learning tasks effectively.The second edition begins with taking you through recipes on evaluating the statistical properties of data and generates synthetic data for machine learning modelling. As you progress through the chapters, you will comes across recipes that will teach you to implement techniques like data pre-processing, linear regression, logistic regression, K-NN, Naïve Bayes, classification, decision trees, Ensembles and much more. Furthermore, you’ll learn to optimize your models with multi-class classification, cross validation, model evaluation and dive deeper in to implementing deep learning with scikit-learn. Along with covering the enhanced features on model section, API and new features like classifiers, regressors and estimators the book also contains recipes on evaluating and fine-tuning the performance of your model. By the end of this book, you will have explored plethora of features offered by scikit-learn for Python to solve any machine learning problem you come across.