Programowanie

Hands-on JavaScript for Python Developers. Leverage your Python knowledge to quickly learn JavaScript and advance your web development career

Sonyl Nagale

Knowledge of Python is a great foundation for learning other languages. This book will help you advance in your software engineering career by leveraging your Python programming skills to learn JavaScript and apply its unique features not only for frontend web development but also for streamlining work on the backend.Starting with the basics of JavaScript, you’ll cover its syntax, its use in the browser, and its frameworks and libraries. From working with user interactions and ingesting data from APIs through to creating APIs with Node.js, this book will help you get up and running with JavaScript using hands-on exercises, code snippets, and detailed descriptions of JavaScript implementation and benefits. To understand the use of JavaScript in the backend, you’ll explore Node.js and discover how it communicates with databases. As you advance, you’ll get to grips with creating your own RESTful APIs and connecting the frontend and backend for holistic full-stack development knowledge.By the end of this Python JavaScript book, you’ll have the knowledge you need to write full-fledged web applications from start to finish. You’ll have also gained hands-on experience of working through several projects, which will help you advance in your career as a JavaScript developer.

Hands-On Kubernetes on Azure. Run your applications securely and at scale on the most widely adopted orchestration platform

Shivakumar Gopalakrishnan, Gunther Lenz

Microsoft is now one of the most significant contributors to Kubernetes open source projects. Kubernetes helps to create, configure, and manage a cluster of virtual machines that are preconfigured to run containerized applications.This book will be your guide to performing successful container orchestration and deployment of Kubernetes clusters on Azure. You will get started by learning how to deploy and manage highly scalable applications, along with understanding how to set up a production-ready Kubernetes cluster on Azure. As you advance, you will learn how to reduce the complexity and operational overheads of managing a Kubernetes cluster on Azure.By the end of this book, you will not only be capable of deploying and managing Kubernetes clusters on Azure with ease, but also have the knowledge of best practices for working with advanced AKS concepts for complex systems.

Hands-On Linux Administration on Azure. Explore the essential Linux administration skills you need to deploy and manage Azure-based workloads

Frederik Vos

Azure’s market share has increased massively and enterprises are adopting it rapidly, while Linux is a widely-used operating system and has proven to be one of the most popular workloads on Azure. It has thus become crucial for Linux administrators and Microsoft professionals to be well versed with managing Linux workloads in an Azure environment.With this guide, system administrators will be able to deploy, automate, and orchestrate containers in Linux on Azure. The book follows a hands-on approach to help you understand DevOps, monitor Linux workloads on Azure and perform advanced system administration. Complete with systematic explanations of concepts, examples and self-assessment questions, the chapters will give you useful insights into Linux and Azure. You’ll explore some of Linux’s advanced features for managing multiple workloads and learn to deploy virtual machines (VMs) in Azure. Dedicated sections will also guide you with managing and extending Azure VMs’ capabilities and understanding automation and orchestration with Ansible and PowerShell DSC. In later chapters, you’ll cover useful Linux troubleshooting and monitoring techniques that will enable you to maintain your workload on Azure.By the end of this book, you’ll be able to make the most out of Azure’s services to efficiently deploy and manage your Linux workloads.

Hands-On Machine Learning for Algorithmic Trading. Design and implement investment strategies based on smart algorithms that learn from data using Python

Stefan Jansen

The explosive growth of digital data has boosted the demand for expertise in trading strategies that use machine learning (ML). This book enables you to use a broad range of supervised and unsupervised algorithms to extract signals from a wide variety of data sources and create powerful investment strategies.This book shows how to access market, fundamental, and alternative data via API or web scraping and offers a framework to evaluate alternative data. You’ll practice the ML work?ow from model design, loss metric definition, and parameter tuning to performance evaluation in a time series context. You will understand ML algorithms such as Bayesian and ensemble methods and manifold learning, and will know how to train and tune these models using pandas, statsmodels, sklearn, PyMC3, xgboost, lightgbm, and catboost. This book also teaches you how to extract features from text data using spaCy, classify news and assign sentiment scores, and to use gensim to model topics and learn word embeddings from financial reports. You will also build and evaluate neural networks, including RNNs and CNNs, using Keras and PyTorch to exploit unstructured data for sophisticated strategies.Finally, you will apply transfer learning to satellite images to predict economic activity and use reinforcement learning to build agents that learn to trade in the OpenAI Gym.

Hands-On Machine Learning for Cybersecurity. Safeguard your system by making your machines intelligent using the Python ecosystem

Soma Halder, Sinan Ozdemir

Cyber threats today are one of the costliest losses that an organization can face. In this book, we use the most efficient tool to solve the big problems that exist in the cybersecurity domain.The book begins by giving you the basics of ML in cybersecurity using Python and its libraries. You will explore various ML domains (such as time series analysis and ensemble modeling) to get your foundations right. You will implement various examples such as building system to identify malicious URLs, and building a program to detect fraudulent emails and spam. Later, you will learn how to make effective use of K-means algorithm to develop a solution to detect and alert you to any malicious activity in the network. Also learn how to implement biometrics and fingerprint to validate whether the user is a legitimate user or not.Finally, you will see how we change the game with TensorFlow and learn how deep learning is effective for creating models and training systems

Hands-On Machine Learning with C++. Build, train, and deploy end-to-end machine learning and deep learning pipelines

Kirill Kolodiazhnyi

C++ can make your machine learning models run faster and more efficiently. This handy guide will help you learn the fundamentals of machine learning (ML), showing you how to use C++ libraries to get the most out of your data. This book makes machine learning with C++ for beginners easy with its example-based approach, demonstrating how to implement supervised and unsupervised ML algorithms through real-world examples.This book will get you hands-on with tuning and optimizing a model for different use cases, assisting you with model selection and the measurement of performance. You’ll cover techniques such as product recommendations, ensemble learning, and anomaly detection using modern C++ libraries such as PyTorch C++ API, Caffe2, Shogun, Shark-ML, mlpack, and dlib. Next, you’ll explore neural networks and deep learning using examples such as image classification and sentiment analysis, which will help you solve various problems. Later, you’ll learn how to handle production and deployment challenges on mobile and cloud platforms, before discovering how to export and import models using the ONNX format.By the end of this C++ book, you will have real-world machine learning and C++ knowledge, as well as the skills to use C++ to build powerful ML systems.

Hands-On Machine Learning with C++. Build, train, and deploy end-to-end machine learning and deep learning pipelines - Second Edition

Kirill Kolodiazhnyi

Written by a seasoned software engineer with several years of industry experience, this book will teach you the basics of machine learning (ML) and show you how to use C++ libraries, along with helping you create supervised and unsupervised ML models.You’ll gain hands-on experience in tuning and optimizing a model for various use cases, enabling you to efficiently select models and measure performance. The chapters cover techniques such as product recommendations, ensemble learning, anomaly detection, sentiment analysis, and object recognition using modern C++ libraries. You’ll also learn how to overcome production and deployment challenges on mobile platforms, and see how the ONNX model format can help you accomplish these tasks.This edition is updated with key topics such as sentiment analysis implementation using transfer learning and transformer-based models, with tracking and visualizing ML experiments with MLflow. An additional section shows how to use Optuna for hyperparameter selection. The section on model deployment into mobile platform includes a detailed explanation of real-time object detection for Android with C++.By the end of this C++ book, you’ll have real-world machine learning and C++ knowledge, as well as the skills to use C++ to build powerful ML systems.*Email sign-up and proof of purchase required

Hands-On Machine Learning with ML.NET. Getting started with Microsoft ML.NET to implement popular machine learning algorithms in C#

Jarred Capellman

Machine learning (ML) is widely used in many industries such as science, healthcare, and research and its popularity is only growing. In March 2018, Microsoft introduced ML.NET to help .NET enthusiasts in working with ML. With this book, you’ll explore how to build ML.NET applications with the various ML models available using C# code.The book starts by giving you an overview of ML and the types of ML algorithms used, along with covering what ML.NET is and why you need it to build ML apps. You’ll then explore the ML.NET framework, its components, and APIs. The book will serve as a practical guide to helping you build smart apps using the ML.NET library. You’ll gradually become well versed in how to implement ML algorithms such as regression, classification, and clustering with real-world examples and datasets. Each chapter will cover the practical implementation, showing you how to implement ML within .NET applications. You’ll also learn to integrate TensorFlow in ML.NET applications. Later you’ll discover how to store the regression model housing price prediction result to the database and display the real-time predicted results from the database on your web application using ASP.NET Core Blazor and SignalR.By the end of this book, you’ll have learned how to confidently perform basic to advanced-level machine learning tasks in ML.NET.

Hands-On Machine Learning with ML.NET. Getting started with Microsoft ML.NET to implement popular machine learning algorithms in C#

Jarred Capellman

Machine learning (ML) is widely used in many industries such as science, healthcare, and research and its popularity is only growing. In March 2018, Microsoft introduced ML.NET to help .NET enthusiasts in working with ML. With this book, you’ll explore how to build ML.NET applications with the various ML models available using C# code.The book starts by giving you an overview of ML and the types of ML algorithms used, along with covering what ML.NET is and why you need it to build ML apps. You’ll then explore the ML.NET framework, its components, and APIs. The book will serve as a practical guide to helping you build smart apps using the ML.NET library. You’ll gradually become well versed in how to implement ML algorithms such as regression, classification, and clustering with real-world examples and datasets. Each chapter will cover the practical implementation, showing you how to implement ML within .NET applications. You’ll also learn to integrate TensorFlow in ML.NET applications. Later you’ll discover how to store the regression model housing price prediction result to the database and display the real-time predicted results from the database on your web application using ASP.NET Core Blazor and SignalR.By the end of this book, you’ll have learned how to confidently perform basic to advanced-level machine learning tasks in ML.NET.