Web Development with MongoDB and Node. Build fast web applications for handling any kind of data - Third Edition

Bruno Joseph D'mello

Node.js builds fast, scalable network applications while MongoDB is the perfect fit as a high-performance, open source NoSQL database solution. The combination of these two technologies offers high performance and scalability and helps in building fast, scalable network applications. Together they provide the power for manage any form of data as well as speed of delivery.This book will help you to get these two technologies working together to build web applications quickly and easily, with effortless deployment to the cloud. You will also learn about angular 4, which consumes pure JSON APOIs from a hapi server.The book begins by setting up your development environment, running you through the steps necessary to get the main application server up-and-running. Then you will see how to use Node.js to connect to a MongoDB database and perform data manipulations.From here on, the book will take you through integration with third-party tools to interact with web apps. You will see how to use controllers and view models to generate reusable code that will reduce development time. Toward the end, the book supplies tests to properly execute your code and take your skills to the next level with the most popular frameworks for developing web applications.By the end of the book, you will have a running web application developed with MongoDB, Node.js, and some of the most powerful and popular frameworks.

Matplotlib 2.x By Example. Multi-dimensional charts, graphs, and plots in Python

Allen Yu, Claire Chung, Aldrin Yim

Big data analytics are driving innovations in scientific research, digital marketing, policy-making and much more. Matplotlib offers simple but powerful plotting interface, versatile plot types and robust customization.Matplotlib 2.x By Example illustrates the methods and applications of various plot types through real world examples. It begins by giving readers the basic know-how on how to create and customize plots by Matplotlib. It further covers how to plot different types of economic data in the form of 2D and 3D graphs, which give insights from a deluge of data from public repositories, such as Quandl Finance. You will learn to visualize geographical data on maps and implement interactive charts.By the end of this book, you will become well versed with Matplotlib in your day-to-day work to perform advanced data visualization. This book will guide you to prepare high quality figures for manuscripts and presentations. You will learn to create intuitive info-graphics and reshaping your message crisply understandable.

Reactive Programming with Swift 4. Build asynchronous reactive applications with easy-to-maintain and clean code using RxSwift and Xcode 9

Navdeep Singh

RxSwift belongs to a large family of Rx implementations in different programming languages that share almost identical syntax and semantics. Reactive approach will help you to write clean, cohesive, resilient, scalable, and maintainable code with highly configurable behavior.This book will introduce you to the world of reactive programming, primarily focusing on mobile platforms. It will tell how you can benefit from using RxSwift in your projects, existing or new. Further on, the book will demonstrate the unbelievable ease of configuring asynchronous behavior and other aspects of the app that are traditionally considered to be hard to implement and maintain. It will explain what Rx is made of, and how to switch to reactive way of thinking to get the most out of it. Also, test production code using RxTest and the red/ green approach. Finally, the book will dive into real-world recipes and show you how to build a real-world app by applying the reactive paradigm.By the end of the book, you’ll be able to build a reactive swift application by leveraging all the concepts this book takes you through.

Industrial Internet Application Development. Simplify IIoT development using public cloud and native cloud services

Alena Traukina, Jayant Thomas, Prashant Tyagi, Veera Kishore Reddipalli

The Industrial Internet refers to the integration of complex physical machines with networked sensors and software. The growth in the number of sensors used in industrial machinery has led to an exponential increase in data being captured for predictive analytics.Industrial Internet Application Development is a practical guide for developers who want to create applications that leverage the full capabilities of IIoT. You will get started by learning how to develop your first IIoT application and understanding its deployment and security. Once you’re familiar with what IIoT is, you will move on to exploring Edge Development along with the analytics aspect of the IIoT stack. In later chapters, you’ll get to grips with the deployment of IIoT applications on the Predix platform. As you cover these concepts, you’ll be able to identify key elements of the development framework and understand their importance while considering architecture and design for IIoT applications.By the end of this book, you will have the skills you need to deploy IIoT applications on the Predix platform and incorporate best practices for developing fault-tolerant and reliable IIoT systems.

Hands-On Deep Learning for Games. Leverage the power of neural networks and reinforcement learning to build intelligent games

Micheal Lanham

The number of applications of deep learning and neural networks has multiplied in the last couple of years. Neural nets has enabled significant breakthroughs in everything from computer vision, voice generation, voice recognition and self-driving cars. Game development is also a key area where these techniques are being applied. This book will give an in depth view of the potential of deep learning and neural networks in game development. We will take a look at the foundations of multi-layer perceptron’s to using convolutional and recurrent networks. In applications from GANs that create music or textures to self-driving cars and chatbots. Then we introduce deep reinforcement learning through the multi-armed bandit problem and other OpenAI Gym environments.As we progress through the book we will gain insights about DRL techniques such as Motivated Reinforcement Learning with Curiosity and Curriculum Learning. We also take a closer look at deep reinforcement learning and in particular the Unity ML-Agents toolkit. By the end of the book, we will look at how to apply DRL and the ML-Agents toolkit to enhance, test and automate your games or simulations. Finally, we will cover your possible next steps and possible areas for future learning.

Python High Performance. Build high-performing, concurrent, and distributed applications - Second Edition

Dr. Gabriele Lanaro

Python is a versatile language that has found applications in many industries. The clean syntax, rich standard library, and vast selection of third-party libraries make Python a wildly popular language. Python High Performance is a practical guide that shows how to leverage the power of both native and third-party Python libraries to build robust applications. The book explains how to use various profilers to find performance bottlenecks and apply the correct algorithm to fix them. The reader will learn how to effectively use NumPy and Cython to speed up numerical code. The book explains concepts of concurrent programming and how to implement robust and responsive applications using Reactive programming. Readers will learn how to write code for parallel architectures using Tensorflow and Theano, and use a cluster of computers for large-scale computations using technologies such as Dask and PySpark. By the end of the book, readers will have learned to achieve performance and scale from their Python applications.

Machine Learning with TensorFlow 1.x. Second generation machine learning with Google's brainchild - TensorFlow 1.x

Saif Ahmed, Quan Hua, Shams Ul Azeem

Google's TensorFlow is a game changer in the world of machine learning. It has made machine learning faster, simpler, and more accessible than ever before. This book will teach you how to easily get started with machine learning using the power of Python and TensorFlow 1.x. Firstly, you’ll cover the basic installation procedure and explore the capabilities of TensorFlow 1.x. This is followed by training and running the first classifier, and coverage of the unique features of the library including data ?ow graphs, training, and the visualization of performance with TensorBoard—all within an example-rich context using problems from multiple industries. You’ll be able to further explore text and image analysis, and be introduced to CNN models and their setup in TensorFlow 1.x. Next, you’ll implement a complete real-life production system from training to serving a deep learning model. As you advance you’ll learn about Amazon Web Services (AWS) and create a deep neural network to solve a video action recognition problem. Lastly, you’ll convert the Caffe model to TensorFlow and be introduced to the high-level TensorFlow library, TensorFlow-Slim.By the end of this book, you will be geared up to take on any challenges of implementing TensorFlow 1.x in your machine learning environment.

Jupyter for Data Science. Exploratory analysis, statistical modeling, machine learning, and data visualization with Jupyter

Dan Toomey

Jupyter Notebook is a web-based environment that enables interactive computing in notebook documents. It allows you to create documents that contain live code, equations, and visualizations. This book is a comprehensive guide to getting started with data science using the popular Jupyter notebook. If you are familiar with Jupyter notebook and want to learn how to use its capabilities to perform various data science tasks, this is the book for you! From data exploration to visualization, this book will take you through every step of the way in implementing an effective data science pipeline using Jupyter. You will also see how you can utilize Jupyter's features to share your documents and codes with your colleagues. The book also explains how Python 3, R, and Julia can be integrated with Jupyter for various data science tasks.By the end of this book, you will comfortably leverage the power of Jupyter to perform various tasks in data science successfully.