Wydawca: 16
Rafał Świdziński, Alexander Kushnir
Modern CMake for C++ isn't just another reference book, or a repackaging of the documentation, but a blueprint to bridging the gap between learning C++ and being able to use it in a professional setting. It's an end-to-end guide to the automation of complex tasks, including building, testing, and packaging software.This second edition is significantly rewritten, restructured and refreshed with latest additions to CMake, such as support of C++20 Modules.In this book, you'll not only learn how to use the CMake language in CMake projects but also discover how to make those projects maintainable, elegant, and clean. As you progress, you'll dive into the structure of source directories, building targets, and packages, all while learning how to compile and link executables and libraries. You'll also gain a deeper understanding of how those processes work and how to optimize builds in CMake for the best results. You'll discover how to use external dependencies in your project – third-party libraries, testing frameworks, program analysis tools, and documentation generators. Finally, you'll gain profi ciency in exporting, installing, and packaging for internal and external purposes.By the end of this book, you'll be able to use CMake confi dently at a professional level.
Jim Ledin
Are you a software developer, systems designer, or computer architecture student looking for a methodical introduction to digital device architectures but overwhelmed by their complexity? This book will help you to learn how modern computer systems work, from the lowest level of transistor switching to the macro view of collaborating multiprocessor servers. You'll gain unique insights into the internal behavior of processors that execute the code developed in high-level languages and enable you to design more efficient and scalable software systems.The book will teach you the fundamentals of computer systems including transistors, logic gates, sequential logic, and instruction operations. You will learn details of modern processor architectures and instruction sets including x86, x64, ARM, and RISC-V. You will see how to implement a RISC-V processor in a low-cost FPGA board and how to write a quantum computing program and run it on an actual quantum computer. By the end of this book, you will have a thorough understanding of modern processor and computer architectures and the future directions these architectures are likely to take.
Jim Ledin, Dave Farley
Are you a software developer, systems designer, or computer architecture student looking for a methodical introduction to digital device architectures, but are overwhelmed by the complexity of modern systems? This step-by-step guide will teach you how modern computer systems work with the help of practical examples and exercises. You’ll gain insights into the internal behavior of processors down to the circuit level and will understand how the hardware executes code developed in high-level languages.This book will teach you the fundamentals of computer systems including transistors, logic gates, sequential logic, and instruction pipelines. You will learn details of modern processor architectures and instruction sets including x86, x64, ARM, and RISC-V. You will see how to implement a RISC-V processor in a low-cost FPGA board and write a quantum computing program and run it on an actual quantum computer.This edition has been updated to cover the architecture and design principles underlying the important domains of cybersecurity, blockchain and bitcoin mining, and self-driving vehicles.By the end of this book, you will have a thorough understanding of modern processors and computer architecture and the future directions these technologies are likely to take.
V Kishore Ayyadevara, Yeshwanth Reddy
Whether you are a beginner or are looking to progress in your computer vision career, this book guides you through the fundamentals of neural networks (NNs) and PyTorch and how to implement state-of-the-art architectures for real-world tasks.The second edition of Modern Computer Vision with PyTorch is fully updated to explain and provide practical examples of the latest multimodal models, CLIP, and Stable Diffusion.You’ll discover best practices for working with images, tweaking hyperparameters, and moving models into production. As you progress, you'll implement various use cases for facial keypoint recognition, multi-object detection, segmentation, and human pose detection. This book provides a solid foundation in image generation as you explore different GAN architectures. You’ll leverage transformer-based architectures like ViT, TrOCR, BLIP2, and LayoutLM to perform various real-world tasks and build a diffusion model from scratch. Additionally, you’ll utilize foundation models' capabilities to perform zero-shot object detection and image segmentation. Finally, you’ll learn best practices for deploying a model to production.By the end of this deep learning book, you'll confidently leverage modern NN architectures to solve real-world computer vision problems.
V Kishore Ayyadevara, Yeshwanth Reddy
Deep learning is the driving force behind many recent advances in various computer vision (CV) applications. This book takes a hands-on approach to help you to solve over 50 CV problems using PyTorch1.x on real-world datasets.You’ll start by building a neural network (NN) from scratch using NumPy and PyTorch and discover best practices for tweaking its hyperparameters. You’ll then perform image classification using convolutional neural networks and transfer learning and understand how they work. As you progress, you’ll implement multiple use cases of 2D and 3D multi-object detection, segmentation, human-pose-estimation by learning about the R-CNN family, SSD, YOLO, U-Net architectures, and the Detectron2 platform. The book will also guide you in performing facial expression swapping, generating new faces, and manipulating facial expressions as you explore autoencoders and modern generative adversarial networks. You’ll learn how to combine CV with NLP techniques, such as LSTM and transformer, and RL techniques, such as Deep Q-learning, to implement OCR, image captioning, object detection, and a self-driving car agent. Finally, you'll move your NN model to production on the AWS Cloud.By the end of this book, you’ll be able to leverage modern NN architectures to solve over 50 real-world CV problems confidently.
Lisa Bock
In today's world, it is important to have confidence in your data storage and transmission strategy. Cryptography can provide you with this confidentiality, integrity, authentication, and non-repudiation. But are you aware of just what exactly is involved in using cryptographic techniques? Modern Cryptography for Cybersecurity Professionals helps you to gain a better understanding of the cryptographic elements necessary to secure your data.The book begins by helping you to understand why we need to secure data and how encryption can provide protection, whether it be in motion or at rest. You'll then delve into symmetric and asymmetric encryption and discover how a hash is used. As you advance, you'll see how the public key infrastructure (PKI) and certificates build trust between parties, so that we can confidently encrypt and exchange data. Finally, you'll explore the practical applications of cryptographic techniques, including passwords, email, and blockchain technology, along with securely transmitting data using a virtual private network (VPN).By the end of this cryptography book, you'll have gained a solid understanding of cryptographic techniques and terms, learned how symmetric and asymmetric encryption and hashed are used, and recognized the importance of key management and the PKI.
Modern Data Architecture on AWS. A Practical Guide for Building Next-Gen Data Platforms on AWS
Behram Irani
Many IT leaders and professionals are adept at extracting data from a particular type of database and deriving value from it. However, designing and implementing an enterprise-wide holistic data platform with purpose-built data services, all seamlessly working in tandem with the least amount of manual intervention, still poses a challenge.This book will help you explore end-to-end solutions to common data, analytics, and AI/ML use cases by leveraging AWS services. The chapters systematically take you through all the building blocks of a modern data platform, including data lakes, data warehouses, data ingestion patterns, data consumption patterns, data governance, and AI/ML patterns. Using real-world use cases, each chapter highlights the features and functionalities of numerous AWS services to enable you to create a scalable, flexible, performant, and cost-effective modern data platform.By the end of this book, you’ll be equipped with all the necessary architectural patterns and be able to apply this knowledge to efficiently build a modern data platform for your organization using AWS services.
Brian Lipp
Modern Data Architectures with Python will teach you how to seamlessly incorporate your machine learning and data science work streams into your open data platforms. You’ll learn how to take your data and create open lakehouses that work with any technology using tried-and-true techniques, including the medallion architecture and Delta Lake.Starting with the fundamentals, this book will help you build pipelines on Databricks, an open data platform, using SQL and Python. You’ll gain an understanding of notebooks and applications written in Python using standard software engineering tools such as git, pre-commit, Jenkins, and Github. Next, you’ll delve into streaming and batch-based data processing using Apache Spark and Confluent Kafka. As you advance, you’ll learn how to deploy your resources using infrastructure as code and how to automate your workflows and code development. Since any data platform's ability to handle and work with AI and ML is a vital component, you’ll also explore the basics of ML and how to work with modern MLOps tooling. Finally, you’ll get hands-on experience with Apache Spark, one of the key data technologies in today’s market.By the end of this book, you’ll have amassed a wealth of practical and theoretical knowledge to build, manage, orchestrate, and architect your data ecosystems.