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GitOps and Kubernetes
GitOps and Kubernetes

GitOps and Kubernetes introduces a radical idea - managing your infrastructure with the same Git pull requests you use to manage your codebase. In this in-depth tutorial, you'll learn to operate infrastructures based on powerful-but-complex technologies such as Kubernetes with the same Git version control tools most developers use daily. With these GitOps techniques and best practices, you'll accelerate application development without compromising on security, easily roll back infrastructure changes, and seamlessly introduce new team members to your automation process. With GitOps you use the Git version control system to organize and manage your infrastructure just like any other codebase. It's an excellent model for applications deployed as containers and pods on Kubernetes. GitOps and Kubernetes teaches you how to use Git and the GitOps methodology to manage a Kubernetes cluster. The book interleaves theory with practice, presenting core Ops concepts alongside easy-to-implemen ...
React Hooks in Action
React Hooks in Action

Build stylish, slick, and speedy-to-load user interfaces in React without writing custom classes. React Hooks are a new category of functions that help you to manage state, lifecycle, and side effects within functional components. React Hooks in Action teaches you to use pre-built hooks like useState, useReducer and useEffect to build your own hooks. Your code will be more reusable, require less boilerplate, and you'll instantly be a more effective React developer. Get started with React Hooks and you'll soon have code that's better organized and easier to maintain. React Hooks are targeted JavaScript functions that let you reuse and share functionality across components. Use them to split components into smaller functions, manage state and side effects, and access React features without classes - all without having to rearrange your component hierarchy. React Hooks in Action teaches you to write fast and reusable React components using Hooks. You'll start by learning to create c ...
Data Pipelines with Apache Airflow
Data Pipelines with Apache Airflow

A successful pipeline moves data efficiently, minimizing pauses and blockages between tasks, keeping every process along the way operational. Apache Airflow provides a single customizable environment for building and managing data pipelines, eliminating the need for a hodgepodge collection of tools, snowflake code, and homegrown processes. Using real-world scenarios and examples, Data Pipelines with Apache Airflow teaches you how to simplify and automate data pipelines, reduce operational overhead, and smoothly integrate all the technologies in your stack. Data pipelines manage the flow of data from initial collection through consolidation, cleaning, analysis, visualization, and more. Apache Airflow provides a single platform you can use to design, implement, monitor, and maintain your pipelines. Its easy-to-use UI, plug-and-play options, and flexible Python scripting make Airflow perfect for any data management task. Data Pipelines with Apache Airflow teaches you how to build an ...
Generative AI with Python and TensorFlow 2
Generative AI with Python and TensorFlow 2

In recent years, generative artificial intelligence has been instrumental in the creation of lifelike data (images, speech, video, music, and text) from scratch. In this book you will unpack how these powerful models are created from relatively simple building blocks, and how you might adapt these models to your own use cases. You will begin by setting up clean containerized environments for Python and getting to grips with the fundamentals of deep neural networks, learning about core concepts like the perceptron, activation functions, backpropagation, and how they all tie together. Once you have covered the basics, you will explore deep generative models in depth, including OpenAI's GPT-series of news generators, networks for style transfer and deepfakes, and synergy with reinforcement learning. As you progress, you will focus on abstractions where useful, and understand the "nuts and bolts" of how the models are composed in code, underpinned by detailed architecture diagrams. T ...
Hands-On Financial Trading with Python
Hands-On Financial Trading with Python

Algorithmic trading helps you stay ahead of the markets by devising strategies in quantitative analysis to gain profits and cut losses. The book starts by introducing you to algorithmic trading and explaining why Python is the best platform for developing trading strategies. You'll then cover quantitative analysis using Python, and learn how to build algorithmic trading strategies with Zipline using various market data sources. Using Zipline as the backtesting library allows access to complimentary US historical daily market data until 2018. As you advance, you will gain an in-depth understanding of Python libraries such as NumPy and pandas for analyzing financial datasets, and explore Matplotlib, statsmodels, and scikit-learn libraries for advanced analytics. You'll also focus on time series forecasting, covering pmdarima and Facebook Prophet. By the end of this trading book, you will be able to build predictive trading signals, adopt basic and advanced algorithmic trading strat ...
Classical Object-Oriented Programming with ECMAScript
Classical Object-Oriented Programming with ECMAScript

ECMAScript (more popularly known by the name "JavaScript") is the language of the web. In the decades past, it has been used to augment web pages with trivial features and obnoxious gimmicks. Today, the language is used to write full-featured web applications that rival modern desktop software in nearly every regard and has even expanded to create desktop and server software. With increased usage, there is a desire to apply more familiar development paradigms while continuing to take advantage of the language's incredibly flexible functional and prototypal models. Of all of the modern paradigms, one of the most influential and widely adopted is the Classical Object-Oriented paradigm, as represented in languages such as Java, C++, Python, Perl, PHP and others. ECMAScript, as an object-oriented language, contains many features familiar to Classical OO developers. However, certain features remain elusive. This paper will detail the development of a classical object-oriented framework f ...
Convolutional Neural Networks with Swift for Tensorflow
Convolutional Neural Networks with Swift for Tensorflow

Dive into and apply practical machine learning and dataset categorization techniques while learning Tensorflow and deep learning. This book uses convolutional neural networks to do image recognition all in the familiar and easy to work with Swift language. It begins with a basic machine learning overview and then ramps up to neural networks and convolutions and how they work. Using Swift and Tensorflow, you'll perform data augmentation, build and train large networks, and build networks for mobile devices. You'll also cover cloud training and the network you build can categorize greyscale data, such as mnist, to large scale modern approaches that can categorize large datasets, such as imagenet. Convolutional Neural Networks with Swift for Tensorflow uses a simple approach that adds progressive layers of complexity until you have arrived at the current state of the art for this field. ...
Deep Reinforcement Learning in Unity
Deep Reinforcement Learning in Unity

Gain an in-depth overview of reinforcement learning for autonomous agents in game development with Unity. This book starts with an introduction to state-based reinforcement learning algorithms involving Markov models, Bellman equations, and writing custom C# code with the aim of contrasting value and policy-based functions in reinforcement learning. Then, you will move on to path finding and navigation meshes in Unity, setting up the ML Agents Toolkit (including how to install and set up ML agents from the GitHub repository), and installing fundamental machine learning libraries and frameworks (such as Tensorflow). You will learn about: deep learning and work through an introduction to Tensorflow for writing neural networks (including perceptron, convolution, and LSTM networks), Q learning with Unity ML agents, and porting trained neural network models in Unity through the Python-C# API. You will also explore the OpenAI Gym Environment used throughout the book. Deep Reinforcement ...
Developing Web Components with TypeScript
Developing Web Components with TypeScript

Create professional and progressive web apps with the native HTML API on the latest technology stack. This book describes the basics of web components and how to create them using plain JavaScript as well as how to make professional applications based on web components using TypeScript. Developing Web Components with TypeScript looks at APIs using examples, techniques, and tricks. You will start with a brief introduction to web components, including slots and templates, handling custom events, and styling components with or without shadow DOM. Then, it introduces TypeScript as part of the tool set. It shows the internal construction of a professional thin library. It also helps you learn how to deal with web components in real-life projects; this includes techniques such as creating a single-page app without framework code. All code samples used here are supported by all modern browsers for you to follow along. ...
Data Science Revealed
Data Science Revealed

Get insight into data science techniques such as data engineering and visualization, statistical modeling, machine learning, and deep learning. This book teaches you how to select variables, optimize hyper parameters, develop pipelines, and train, test, and validate machine and deep learning models. Each chapter includes a set of examples allowing you to understand the concepts, assumptions, and procedures behind each model. The book covers parametric methods or linear models that combat under- or over-fitting using techniques such as Lasso and Ridge. It includes complex regression analysis with time series smoothing, decomposition, and forecasting. It takes a fresh look at non-parametric models for binary classification (logistic regression analysis) and ensemble methods such as decision trees, support vector machines, and naive Bayes. It covers the most popular non-parametric method for time-event data (the Kaplan-Meier estimator). It also covers ways of solving classification pro ...
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