Wydawca: 16
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.
Gaurav Agarwal
Containers have entirely changed how developers and end-users see applications as a whole. With this book, you'll learn all about containers, their architecture and benefits, and how to implement them within your development lifecycle.You'll discover how you can transition from the traditional world of virtual machines and adopt modern ways of using DevOps to ship a package of software continuously. Starting with a quick refresher on the core concepts of containers, you'll move on to study the architectural concepts to implement modern ways of application development. You'll cover topics around Docker, Kubernetes, Ansible, Terraform, Packer, and other similar tools that will help you to build a base. As you advance, the book covers the core elements of cloud integration (AWS ECS, GKE, and other CaaS services), continuous integration, and continuous delivery (GitHub actions, Jenkins, and Spinnaker) to help you understand the essence of container management and delivery. The later sections of the book will take you through container pipeline security and GitOps (Flux CD and Terraform).By the end of this DevOps book, you'll have learned best practices for automating your development lifecycle and making the most of containers, infrastructure automation, and CaaS, and be ready to develop applications using modern tools and techniques.
Gaurav Agarwal
DevOps and the cloud have changed how we look at software development and operations like never before, leading to the rapid growth of various DevOps tools, techniques, and practices. This updated edition helps you pick up the right tools by providing you with everything you need to get started with your DevOps journey.The book begins by introducing you to modern cloud-native architecture, and then teaches you about the architectural concepts needed to implement the modern way of application development. The next set of chapters helps you get familiarized with Git, Docker, Kubernetes, Ansible, Terraform, Packer, and other similar tools to enable you to build a base. As you advance, you’ll explore the core elements of cloud integration—AWS ECS, GKE, and other CaaS services. The chapters also discuss GitOps, continuous integration, and continuous delivery—GitHub actions, Jenkins, and Argo CD—to help you understand the essence of modern app delivery. Later, you’ll operate your container app in production using a service mesh and apply AI in DevOps. Throughout the book, you’ll discover best practices for automating and managing your development lifecycle, infrastructure, containers, and more.By the end of this DevOps book, you'll be well-equipped to develop and operate applications using modern tools and techniques.
Liudmila Molkova, Sergey Kanzhelev
As distributed systems become more complex and dynamic, their observability needs to grow to aid the development of holistic solutions for performance or usage analysis and debugging. Distributed tracing brings structure, correlation, causation, and consistency to your telemetry, thus allowing you to answer arbitrary questions about your system and creating a foundation for observability vendors to build visualizations and analytics.Modern Distributed Tracing in .NET is your comprehensive guide to observability that focuses on tracing and performance analysis using a combination of telemetry signals and diagnostic tools. You'll begin by learning how to instrument your apps automatically as well as manually in a vendor-neutral way. Next, you’ll explore how to produce useful traces and metrics for typical cloud patterns and get insights into your system and investigate functional, configurational, and performance issues. The book is filled with instrumentation examples that help you grasp how to enrich auto-generated telemetry or produce your own to get the level of detail your system needs, along with controlling your costs with sampling, aggregation, and verbosity.By the end of this book, you'll be ready to adopt and leverage tracing and other observability signals and tools and tailor them to your needs as your system evolves.