Programowanie

Applied Deep Learning and Computer Vision for Self-Driving Cars. Build autonomous vehicles using deep neural networks and behavior-cloning techniques

Balu Nair, Sumit Ranjan, Dr. S. Senthamilarasu

Thanks to a number of recent breakthroughs, self-driving car technology is now an emerging subject in the field of artificial intelligence and has shifted data scientists' focus to building autonomous cars that will transform the automotive industry. This book is a comprehensive guide to use deep learning and computer vision techniques to develop autonomous cars. Starting with the basics of self-driving cars (SDCs), this book will take you through the deep neural network techniques required to get up and running with building your autonomous vehicle. Once you are comfortable with the basics, you'll delve into advanced computer vision techniques and learn how to use deep learning methods to perform a variety of computer vision tasks such as finding lane lines, improving image classification, and so on. You will explore the basic structure and working of a semantic segmentation model and get to grips with detecting cars using semantic segmentation. The book also covers advanced applications such as behavior-cloning and vehicle detection using OpenCV, transfer learning, and deep learning methodologies to train SDCs to mimic human driving.By the end of this book, you'll have learned how to implement a variety of neural networks to develop your own autonomous vehicle using modern Python libraries.

Applied Deep Learning with Python. Use scikit-learn, TensorFlow, and Keras to create intelligent systems and machine learning solutions

Alex Galea, Luis Capelo

Taking an approach that uses the latest developments in the Python ecosystem, you’ll first be guided through the Jupyter ecosystem, key visualization libraries and powerful data sanitization techniques before you train your first predictive model. You’ll then explore a variety of approaches to classification such as support vector networks, random decision forests and k-nearest neighbors to build on your knowledge before moving on to advanced topics.After covering classification, you’ll go on to discover ethical web scraping and interactive visualizations, which will help you professionally gather and present your analysis. Next, you’ll start building your keystone deep learning application, one that aims to predict the future price of Bitcoin based on historical public data. You’ll then be guided through a trained neural network, which will help you explore common deep learning network architectures (convolutional, recurrent, and generative adversarial networks) and deep reinforcement learning. Later, you’ll delve into model optimization and evaluation. You’ll do all this while working on a production-ready web application that combines TensorFlow and Keras to produce meaningful user-friendly results.By the end of this book, you’ll be equipped with the skills you need to tackle and develop your own real-world deep learning projects confidently and effectively.

Applied Machine Learning and High-Performance Computing on AWS. Accelerate the development of machine learning applications following architectural best practices

Mani Khanuja, Farooq Sabir , Shreyas Subramanian,...

Machine learning (ML) and high-performance computing (HPC) on AWS run compute-intensive workloads across industries and emerging applications. Its use cases can be linked to various verticals, such as computational fluid dynamics (CFD), genomics, and autonomous vehicles.This book provides end-to-end guidance, starting with HPC concepts for storage and networking. It then progresses to working examples on how to process large datasets using SageMaker Studio and EMR. Next, you’ll learn how to build, train, and deploy large models using distributed training. Later chapters also guide you through deploying models to edge devices using SageMaker and IoT Greengrass, and performance optimization of ML models, for low latency use cases.By the end of this book, you’ll be able to build, train, and deploy your own large-scale ML application, using HPC on AWS, following industry best practices and addressing the key pain points encountered in the application life cycle.

Applied SOA Patterns on the Oracle Platform. Fuse together your pragmatic Oracle experience with abstract SOA patterns with this practical guide

Sergey Popov

Applied SOA Patterns on the Oracle Platform is aimed at architects practicing SOA or traditional integration, and also at technical team leaders implementing Oracle Fusion under SCRUM or WF methodology.

Applied Supervised Learning with Python. Use scikit-learn to build predictive models from real-world datasets and prepare yourself for the future of machine learning

Benjamin Johnston, Ishita Mathur

Machine learning—the ability of a machine to give right answers based on input data—has revolutionized the way we do business. Applied Supervised Learning with Python provides a rich understanding of how you can apply machine learning techniques in your data science projects using Python. You'll explore Jupyter Notebooks, the technology used commonly in academic and commercial circles with in-line code running support.With the help of fun examples, you'll gain experience working on the Python machine learning toolkit—from performing basic data cleaning and processing to working with a range of regression and classification algorithms. Once you’ve grasped the basics, you'll learn how to build and train your own models using advanced techniques such as decision trees, ensemble modeling, validation, and error metrics. You'll also learn data visualization techniques using powerful Python libraries such as Matplotlib and Seaborn. This book also covers ensemble modeling and random forest classifiers along with other methods for combining results from multiple models, and concludes by delving into cross-validation to test your algorithm and check how well the model works on unseen data.By the end of this book, you'll be equipped to not only work with machine learning algorithms, but also be able to create some of your own!

Applied Unsupervised Learning with Python. Discover hidden patterns and relationships in unstructured data with Python

Benjamin Johnston, Aaron Jones , Christopher Kruger

Unsupervised learning is a useful and practical solution in situations where labeled data is not available.Applied Unsupervised Learning with Python guides you in learning the best practices for using unsupervised learning techniques in tandem with Python libraries and extracting meaningful information from unstructured data. The book begins by explaining how basic clustering works to find similar data points in a set. Once you are well-versed with the k-means algorithm and how it operates, you’ll learn what dimensionality reduction is and where to apply it. As you progress, you’ll learn various neural network techniques and how they can improve your model. While studying the applications of unsupervised learning, you will also understand how to mine topics that are trending on Twitter and Facebook and build a news recommendation engine for users. Finally, you will be able to put your knowledge to work through interesting activities such as performing a Market Basket Analysis and identifying relationships between different products.By the end of this book, you will have the skills you need to confidently build your own models using Python.

Applying Math with Python. Practical recipes for solving computational math problems using Python programming and its libraries

Sam Morley

Python, one of the world's most popular programming languages, has a number of powerful packages to help you tackle complex mathematical problems in a simple and efficient way. These core capabilities help programmers pave the way for building exciting applications in various domains, such as machine learning and data science, using knowledge in the computational mathematics domain.The book teaches you how to solve problems faced in a wide variety of mathematical fields, including calculus, probability, statistics and data science, graph theory, optimization, and geometry. You'll start by developing core skills and learning about packages covered in Python’s scientific stack, including NumPy, SciPy, and Matplotlib. As you advance, you'll get to grips with more advanced topics of calculus, probability, and networks (graph theory). After you gain a solid understanding of these topics, you'll discover Python's applications in data science and statistics, forecasting, geometry, and optimization. The final chapters will take you through a collection of miscellaneous problems, including working with specific data formats and accelerating code.By the end of this book, you'll have an arsenal of practical coding solutions that can be used and modified to solve a wide range of practical problems in computational mathematics and data science.

Apps and Services with .NET 10. Build practical projects with Avalonia, Blazor, gRPC, GraphQL, and other enterprise technologies - Third Edition

Mark J. Price

Building modern apps and services with C# and .NET isn’t just about knowing the syntax—it’s about knowing the right tools for the job. Whether you’re building for web, desktop, or mobile, the .NET ecosystem offers a vast range of technologies. But with so many choices, it’s easy to get stuck in a narrow skillset. Apps and Services with .NET 10 helps you build real-world experience across the breadth of what .NET has to offer.This edition covers practical implementations across a diverse set of technologies. You'll build APIs with ASP.NET Core Minimal API, gRPC, GraphQL, and SignalR, and create user-facing applications using Blazor for the web, Avalonia for desktop, and .NET MAUI for mobile. You’ll also explore native AOT (Ahead-of-Time) compilation for high-performance web services, along with caching, messaging, and long-running background tasks. Each chapter provides hands-on projects and real-world context for when and why to use each tool.By the end, you’ll have a full-stack command of modern .NET. You’ll know how to select the right UI tech for your target platform, build APIs that scale, architect reliable backend services, and confidently adopt newer patterns. You won’t just understand the tools—you’ll know how to put them together to deliver robust, user-friendly, cross-platform apps in production environments.*Email sign-up and proof of purchase required

Apps and Services with .NET 10. Build practical projects with Avalonia, Blazor, gRPC, GraphQL, and other enterprise technologies - Third Edition

Mark J. Price

Building modern apps and services with C# and .NET isn’t just about knowing the syntax—it’s about knowing the right tools for the job. Whether you’re building for web, desktop, or mobile, the .NET ecosystem offers a vast range of technologies. But with so many choices, it’s easy to get stuck in a narrow skillset. Apps and Services with .NET 10 helps you build real-world experience across the breadth of what .NET has to offer.This edition covers practical implementations across a diverse set of technologies. You'll build APIs with ASP.NET Core Minimal API, gRPC, GraphQL, and SignalR, and create user-facing applications using Blazor for the web, Avalonia for desktop, and .NET MAUI for mobile. You’ll also explore native AOT (Ahead-of-Time) compilation for high-performance web services, along with caching, messaging, and long-running background tasks. Each chapter provides hands-on projects and real-world context for when and why to use each tool.By the end, you’ll have a full-stack command of modern .NET. You’ll know how to select the right UI tech for your target platform, build APIs that scale, architect reliable backend services, and confidently adopt newer patterns. You won’t just understand the tools—you’ll know how to put them together to deliver robust, user-friendly, cross-platform apps in production environments.*Email sign-up and proof of purchase required