Hands-On Android UI Development. Design and develop attractive user interfaces for Android applications

Jason Morris

A great user interface (UI) can spell the difference between success and failure for any new application. This book will show you not just how to code great UIs, but how to design them as well. It will take novice Android developers on a journey, showing them how to leverage the Android platform to produce stunning Android applications. Begin with the basics of creating Android applications and then move on to topics such as screen and layout design. Next, learn about techniques that will help improve performance for your application. Also, explore how to create reactive applications that are fast, animated, and guide the user toward their goals with minimal distraction. Understand Android architecture components and learn how to build your application to automatically respond to changes made by the user. Great platforms are not always enough, so this book also focuses on creating custom components, layout managers, and 2D graphics. Also, explore many tips and best practices to ease your UI development process. By the end, you'll be able to design and build not only amazing UIs, but also systems that provide the best possible user experience.

Practical Computer Vision. Extract insightful information from images using TensorFlow, Keras, and OpenCV

Abhinav Dadhich

In this book, you will find several recently proposed methods in various domains of computer vision. You will start by setting up the proper Python environment to work on practical applications. This includes setting up libraries such as OpenCV, TensorFlow, and Keras using Anaconda. Using these libraries, you'll start to understand the concepts of image transformation and filtering. You will find a detailed explanation of feature detectors such as FAST and ORB; you'll use them to find similar-looking objects.With an introduction to convolutional neural nets, you will learn how to build a deep neural net using Keras and how to use it to classify the Fashion-MNIST dataset. With regard to object detection, you will learn the implementation of a simple face detector as well as the workings of complex deep-learning-based object detectors such as Faster R-CNN and SSD using TensorFlow. You'll get started with semantic segmentation using FCN models and track objects with Deep SORT. Not only this, you will also use Visual SLAM techniques such as ORB-SLAM on a standard dataset. By the end of this book, you will have a firm understanding of the different computer vision techniques and how to apply them in your applications.

Getting Started with Terraform. Manage production infrastructure as a code - Second Edition

Kirill Shirinkin

Terraform is a tool used to efficiently build, configure, and improve the production infrastructure. It can manage the existing infrastructure as well as create custom in-house solutions. This book shows you when and how to implement infrastructure as a code practices with Terraform. It covers everything necessary to set up the complete management of infrastructure with Terraform, starting with the basics of using providers and resources. It is a comprehensive guide that begins with very small infrastructure templates and takes you all the way to managing complex systems, all using concrete examples that evolve over the course of the book. The book ends with the complete workflow of managing a production infrastructure as code—this is achieved with the help of version control and continuous integration. The readers will also learn how to combine multiple providers in a single template and manage different code bases with many complex modules. It focuses on how to set up continuous integration for the infrastructure code.The readers will be able to use Terraform to build, change, and combine infrastructure safely and efficiently.

Deep Learning Essentials. Your hands-on guide to the fundamentals of deep learning and neural network modeling

Wei Di, Jianing Wei, Anurag Bhardwaj

Deep Learning a trending topic in the field of Artificial Intelligence today and can be considered to be an advanced form of machine learning. This book will help you take your first steps in training efficient deep learning models and applying them in various practical scenarios. You will model, train, and deploy different kinds of neural networks such as CNN, RNN, and will see some of their applications in real-world domains including computer vision, natural language processing, speech recognition, and so on. You will build practical projects such as chatbots, implement reinforcement learning to build smart games, and develop expert systems for image captioning and processing using Python library such as TensorFlow. This book also covers solutions for different problems you might come across while training models, such as noisy datasets, and small datasets.By the end of this book, you will have a firm understanding of the basics of deep learning and neural network modeling, along with their practical applications.

Learning Concurrency in Python. Build highly efficient, robust, and concurrent applications

Elliot Forbes

Python is a very high level, general purpose language that is utilized heavily in fields such as data science and research, as well as being one of the top choices for general purpose programming for programmers around the world. It features a wide number of powerful, high and low-level libraries and frameworks that complement its delightful syntax and enable Python programmers to create.This book introduces some of the most popular libraries and frameworks and goes in-depth into how you can leverage these libraries for your own high-concurrent, highly-performant Python programs. We'll cover the fundamental concepts of concurrency needed to be able to write your own concurrent and parallel software systems in Python.The book will guide you down the path to mastering Python concurrency, giving you all the necessary hardware and theoretical knowledge. We'll cover concepts such as debugging and exception handling as well as some of the most popular libraries and frameworks that allow you to create event-driven and reactive systems.By the end of the book, you'll have learned the techniques to write incredibly efficient concurrent systems that follow best practices.

Practical Big Data Analytics. Hands-on techniques to implement enterprise analytics and machine learning using Hadoop, Spark, NoSQL and R

Nataraj Dasgupta

Big Data analytics relates to the strategies used by organizations to collect, organize, and analyze large amounts of data to uncover valuable business insights that cannot be analyzed through traditional systems. Crafting an enterprise-scale cost-efficient Big Data and machine learning solution to uncover insights and value from your organization’s data is a challenge. Today, with hundreds of new Big Data systems, machine learning packages, and BI tools, selecting the right combination of technologies is an even greater challenge. This book will help you do that. With the help of this guide, you will be able to bridge the gap between the theoretical world of technology and the practical reality of building corporate Big Data and data science platforms. You will get hands-on exposure to Hadoop and Spark, build machine learning dashboards using R and R Shiny, create web-based apps using NoSQL databases such as MongoDB, and even learn how to write R code for neural networks.By the end of the book, you will have a very clear and concrete understanding of what Big Data analytics means, how it drives revenues for organizations, and how you can develop your own Big Data analytics solution using the different tools and methods articulatedin this book.

Preparing for the Certified OpenStack Administrator Exam. A complete guide for developers taking tests conducted by the OpenStack Foundation

Matt Dorn

This book provides you with a specific strategy to pass the OpenStack Foundation’s first professional certification: the Certified OpenStack Administrator. In a recent survey, 78% of respondents said the OpenStack skills shortage had deterred them from adopting OpenStack. Consider this an opportunity to increase employer and customer confidence by proving you have the skills required to administrate real-world OpenStack clouds.You will begin your journey by getting well-versed with the OpenStack environment, understanding the benefits of taking the exam, and installing an included OpenStack All-in-One Virtual Appliance to work through objectives covered throughout the book. After exploring the basics of the individual services, you will be introduced to strategies to accomplish the exam objectives relevant to Keystone, Glance, Nova, Neutron, Cinder, Swift, Heat, and troubleshooting.Finally, you’ll benefit from the special tips section and a practice exam to put your knowledge to the test. By the end of the journey, you will be ready to become a Certified OpenStack Administrator!

Hands-On Meta Learning with Python. Meta learning using one-shot learning, MAML, Reptile, and Meta-SGD with TensorFlow

Sudharsan Ravichandiran

Meta learning is an exciting research trend in machine learning, which enables a model to understand the learning process. Unlike other ML paradigms, with meta learning you can learn from small datasets faster.Hands-On Meta Learning with Python starts by explaining the fundamentals of meta learning and helps you understand the concept of learning to learn. You will delve into various one-shot learning algorithms, like siamese, prototypical, relation and memory-augmented networks by implementing them in TensorFlow and Keras. As you make your way through the book, you will dive into state-of-the-art meta learning algorithms such as MAML, Reptile, and CAML. You will then explore how to learn quickly with Meta-SGD and discover how you can perform unsupervised learning using meta learning with CACTUs. In the concluding chapters, you will work through recent trends in meta learning such as adversarial meta learning, task agnostic meta learning, and meta imitation learning.By the end of this book, you will be familiar with state-of-the-art meta learning algorithms and able to enable human-like cognition for your machine learning models.