Informatyka

Deep Learning with PyTorch. A practical approach to building neural network models using PyTorch

Vishnu Subramanian

Deep learning powers the most intelligent systems in the world, such as Google Voice, Siri, and Alexa. Advancements in powerful hardware, such as GPUs, software frameworks such as PyTorch, Keras, TensorFlow, and CNTK along with the availability of big data have made it easier to implement solutions to problems in the areas of text, vision, and advanced analytics. This book will get you up and running with one of the most cutting-edge deep learning libraries—PyTorch. PyTorch is grabbing the attention of deep learning researchers and data science professionals due to its accessibility, efficiency and being more native to Python way of development. You'll start off by installing PyTorch, then quickly move on to learn various fundamental blocks that power modern deep learning. You will also learn how to use CNN, RNN, LSTM and other networks to solve real-world problems. This book explains the concepts of various state-of-the-art deep learning architectures, such as ResNet, DenseNet, Inception, and Seq2Seq, without diving deep into the math behind them. You will also learn about GPU computing during the course of the book. You will see how to train a model with PyTorch and dive into complex neural networks such as generative networks for producing text and images. By the end of the book, you'll be able to implement deep learning applications in PyTorch with ease.

Deep Learning with TensorFlow and Keras - 3rd edition. Build and deploy supervised, unsupervised, deep, and reinforcement learning models - Third Edition

Dr. Amita Kapoor, Antonio Gulli, Sujit Pal

Deep Learning with TensorFlow and Keras teaches you neural networks and deep learning techniques using TensorFlow (TF) and Keras. You'll learn how to write deep learning applications in the most powerful, popular, and scalable machine learning stack available.TensorFlow 2.x focuses on simplicity and ease of use, with updates like eager execution, intuitive higher-level APIs based on Keras, and flexible model building on any platform. This book uses the latest TF 2.0 features and libraries to present an overview of supervised and unsupervised machine learning models and provides a comprehensive analysis of deep learning and reinforcement learning models using practical examples for the cloud, mobile, and large production environments.This book also shows you how to create neural networks with TensorFlow, runs through popular algorithms (regression, convolutional neural networks (CNNs), transformers, generative adversarial networks (GANs), recurrent neural networks (RNNs), natural language processing (NLP), and graph neural networks (GNNs)), covers working example apps, and then dives into TF in production, TF mobile, and TensorFlow with AutoML.

Deep Learning with TensorFlow. Explore neural networks with Python

Giancarlo Zaccone, Fabrizio Milo, Md. Rezaul Karim

Deep learning is the step that comes after machine learning, and has more advancedimplementations. Machine learning is not just for academics anymore, but is becoming a mainstream practice through wide adoption, and deep learning has taken the front seat. As a data scientist, if you want to explore data abstraction layers, this book will be your guide. This book shows how this can be exploited in the real world with complex raw data using TensorFlow 1.x.Throughout the book, you’ll learn how to implement deep learning algorithms for machine learning systems and integrate them into your product offerings, includingsearch, image recognition, and language processing. Additionally, you’ll learn howto analyze and improve the performance of deep learning models. This can be done bycomparing algorithms against benchmarks, along with machine intelligence, to learnfrom the information and determine ideal behaviors within a specific context.After finishing the book, you will be familiar with machine learning techniques, in particular the use of TensorFlow for deep learning, and will be ready to apply your knowledge to research or commercial projects.

Deep Reinforcement Learning Hands-On. Apply modern RL methods, with deep Q-networks, value iteration, policy gradients, TRPO, AlphaGo Zero and more

Oleg Vasilev, Maxim Lapan, Martijn van Otterlo,...

Deep Reinforcement Learning Hands-On is a comprehensive guide to the very latest DL tools and their limitations. You will evaluate methods including Cross-entropy and policy gradients, before applying them to real-world environments. Take on both the Atari set of virtual games and family favorites such as Connect4.The book provides an introduction to the basics of RL, giving you the know-how to code intelligent learning agents to take on a formidable array of practical tasks. Discover how to implement Q-learning on 'grid world' environments, teach your agent to buy and trade stocks, and find out how natural language models are driving the boom in chatbots.

DeepSeek in Practice. From basics to fine-tuning, distillation, agent design, and prompt engineering of open source LLM

Andy Peng, Alex Strick van Linschoten, Duarte...

Learn how to build, fine-tune, and deploy AI systems using DeepSeek, one of the most influential open-source large language models available today. This book guides you through real-world DeepSeek applications—from understanding its core architecture and training foundations to developing reasoning agents and deploying production-ready systems.Starting with a concise synthesis of DeepSeek's research, breakthroughs, and open-source philosophy, you’ll progress to hands-on projects including prompt engineering, workflow design, and rationale distillation. Through detailed case studies—ranging from document understanding to legal clause analysis—you’ll see how to use DeepSeek in high-value GenAI scenarios.You’ll also learn to build sophisticated agent workflows and prepare data for fine-tuning. By the end of the book, you’ll have the skills to integrate DeepSeek into local deployments, cloud CI/CD pipelines, and custom LLMOps environments.Written by experts with deep knowledge of open-source LLMs and deployment ecosystems, this book is your comprehensive guide to DeepSeek’s capabilities and implementation.

Dell VxRail System Design and Best Practices. A complete guide to VxRail appliance design and best practices

Victor Wu

Virtualized systems are well established now, and their disparate components can be found bundled together in hyper-converged infrastructures, such as VxRail from Dell EMC. Dell VxRail System Design and Best Practices will take you, as a system architect or administrator, through the process of designing and protecting VxRail systems.While this book assumes a certain level of knowledge of VMware, vSphere 7.x, and vCenter Server, you’ll get a thorough overview of VxRail's components, features, and architecture, as well as a breakdown of the benefits of this hyper-converged system. This guide will give you an in-depth understanding of VxRail, as well as plenty of practical examples and self-assessment questions along the way to help you plan and design every core component of a VxRail system – from vSAN storage policies to cluster expansion. It's no good having a great system if you lose everything when it breaks, so you'll spend some time examining advanced recovery options, such as VMware Site Recovery Manager and Veeam Backup and Replication.By the end of this book, you will have got to grips with Dell’s hyper-converged VxRail offering, taking your virtualization proficiency to the next level.

Delphi 2007 dla WIN32 i bazy danych

Marian Wybrańczyk

Stwórz własne aplikacje dla systemu Windows Jak pracować ze środowiskiem programistycznym Delphi? W jaki sposób tworzyć biblioteki DLL? Jak zaprojektować wydajną bazę danych? Jak tworzyć aplikacje operujące na bazach danych? Wśród wszystkich środowisk programistycznych umożliwiających tworzenie aplikacji Delphi jest jednym z najbardziej znanych i popularnych. To narzędzie, obecne na rynku od ponad dwunastu lat, cieszy się zasłużonym uznaniem twórców oprogramowania -- dzięki sporym możliwościom, ogromnej bibliotece komponentów i czytelnej składni języka Object Pascal, będącego podstawą tego środowiska. Najnowsza wersja Delphi, oznaczona symbolem RAD Studio 2007, nie tylko umożliwia tworzenie "klasycznych" aplikacji dla Windows, opartych o Windows API, ale także udostępnia kontrolki platformy .NET. Książka "Delphi 2007 dla WIN32 i bazy danych" to podręcznik opisujący zasady tworzenia aplikacji dla systemu Windows w najnowszej wersji Delphi. Przedstawia ona techniki tworzenia aplikacji bazodanowych w oparciu o mechanizmy Windows API i kontrolki VCL. Czytając ją, poznasz komponenty, jakie Delphi oferuje programiście, i dowiesz się, jak korzystać z nich we własnych aplikacjach. Opanujesz mechanizmy komunikacji z niemal wszystkimi systemami zarządzania bazami danych dostępnymi na rynku. Przeczytasz także o tworzeniu wersji instalacyjnych napisanych przez siebie aplikacji. Interfejs użytkownika Delphi 2007 Komponenty dostępne w Delphi Przetwarzanie grafiki Korzystanie z komponentów VCL Aplikacje wielowątkowe Tworzenie bibliotek DLL Operacje na plikach Obsługa dokumentów XML Projektowanie bazy danych i struktury tabel Komunikacja z bazami danych Mechanizmy blokowania rekordów Tworzenie wersji instalacyjnych aplikacji Wykorzystaj możliwości najnowszej wersji środowiska programistycznego, które zrewolucjonizowało proces tworzenia aplikacji!

Delphi Cookbook. 50 hands-on recipes to master the power of Delphi for cross-platform and mobile development on Windows, Mac OS X, Android, and iOS

Daniele Teti

Intended to refresh the basics of Delphi as well as advance your knowledge to the next level, it is assumed you will know RAD studio and the Object Pascal language. However, if you are not an experienced RAD studio programmer this accessible guide will still develop those initial crucial skills.