Programowanie

Deep Learning with PyTorch Lightning. Swiftly build high-performance Artificial Intelligence (AI) models using Python

Kunal Sawarkar, Dheeraj Arremsetty

Building and implementing deep learning (DL) is becoming a key skill for those who want to be at the forefront of progress.But with so much information and complex study materials out there, getting started with DL can feel quite overwhelming.Written by an AI thought leader, Deep Learning with PyTorch Lightning helps researchers build their first DL models quickly and easily without getting stuck on the complexities. With its help, you’ll be able to maximize productivity for DL projects while ensuring full flexibility – from model formulation to implementation.Throughout this book, you’ll learn how to configure PyTorch Lightning on a cloud platform, understand the architectural components, and explore how they are configured to build various industry solutions. You’ll build a neural network architecture, deploy an application from scratch, and see how you can expand it based on your specific needs, beyond what the framework can provide.In the later chapters, you’ll also learn how to implement capabilities to build and train various models like Convolutional Neural Nets (CNN), Natural Language Processing (NLP), Time Series, Self-Supervised Learning, Semi-Supervised Learning, Generative Adversarial Network (GAN) using PyTorch Lightning.By the end of this book, you’ll be able to build and deploy DL models with confidence.

Deep Learning with TensorFlow 2 and Keras. Regression, ConvNets, GANs, RNNs, NLP, and more with TensorFlow 2 and the Keras API - Second Edition

Antonio Gulli, Dr. Amita Kapoor, Sujit Pal

Deep Learning with TensorFlow 2 and Keras, Second Edition teaches neural networks and deep learning techniques alongside 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 is the machine learning library of choice for professional applications, while Keras offers a simple and powerful Python API for accessing TensorFlow. TensorFlow 2 provides full Keras integration, making advanced machine learning easier and more convenient than ever before.This book also introduces neural networks with TensorFlow, runs through the main applications (regression, ConvNets (CNNs), GANs, RNNs, NLP), covers two working example apps, and then dives into TF in production, TF mobile, and using TensorFlow with AutoML.

Deep Learning with Theano. Perform large-scale numerical and scientific computations efficiently

Christopher Bourez

This book offers a complete overview of Deep Learning with Theano, a Python-based library that makes optimizing numerical expressions and deep learning models easy on CPU or GPU.The book provides some practical code examples that help the beginner understand how easy it is to build complex neural networks, while more experimented data scientists will appreciate the reach of the book, addressing supervised and unsupervised learning, generative models, reinforcement learning in the fields of image recognition, natural language processing, or game strategy.The book also discusses image recognition tasks that range from simple digit recognition, image classification, object localization, image segmentation, to image captioning. Natural language processing examples include text generation, chatbots, machine translation, and question answering. The last example deals with generating random data that looks real and solving games such as in the Open-AI gym. At the end, this book sums up the best -performing nets for each task. While early research results were based on deep stacks of neural layers, in particular, convolutional layers, the book presents the principles that improved the efficiency of these architectures, in order to help the reader build new custom nets.

Deep Reinforcement Learning Hands-On. A practical and easy-to-follow guide to RL from Q-learning and DQNs to PPO and RLHF - Third Edition

Maxim Lapan

Start your journey into reinforcement learning (RL) and reward yourself with the third edition of Deep Reinforcement Learning Hands-On. This book takes you through the basics of RL to more advanced concepts with the help of various applications, including game playing, discrete optimization, stock trading, and web browser navigation. By walking you through landmark research papers in the field, this deep RL book will equip you with practical knowledge of RL and the theoretical foundation to understand and implement most modern RL papers. The book retains its approach of providing concise and easy-to-follow explanations from the previous editions. You'll work through practical and diverse examples, from grid environments and games to stock trading and RL agents in web environments, to give you a well-rounded understanding of RL, its capabilities, and its use cases. You'll learn about key topics, such as deep Q-networks (DQNs), policy gradient methods, continuous control problems, and highly scalable, non-gradient methods. If you want to learn about RL through a practical approach using OpenAI Gym and PyTorch, concise explanations, and the incremental development of topics, then Deep Reinforcement Learning Hands-On, Third Edition, is your ideal companion*Email sign-up and proof of purchase required

Deep Reinforcement Learning Hands-On. Apply modern RL methods to practical problems of chatbots, robotics, discrete optimization, web automation, and more - Second Edition

Maxim Lapan

Deep Reinforcement Learning Hands-On, Second Edition is an updated and expanded version of the bestselling guide to the very latest reinforcement learning (RL) tools and techniques. It provides you with an introduction to the fundamentals of RL, along with the hands-on ability to code intelligent learning agents to perform a range of practical tasks.With six new chapters devoted to a variety of up-to-the-minute developments in RL, including discrete optimization (solving the Rubik's Cube), multi-agent methods, Microsoft's TextWorld environment, advanced exploration techniques, and more, you will come away from this book with a deep understanding of the latest innovations in this emerging field.In addition, you will gain actionable insights into such topic areas as deep Q-networks, policy gradient methods, continuous control problems, and highly scalable, non-gradient methods. You will also discover how to build a real hardware robot trained with RL for less than $100 and solve the Pong environment in just 30 minutes of training using step-by-step code optimization.In short, Deep Reinforcement Learning Hands-On, Second Edition, is your companion to navigating the exciting complexities of RL as it helps you attain experience and knowledge through real-world examples.

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.

Deep Reinforcement Learning with Python. Master classic RL, deep RL, distributional RL, inverse RL, and more with OpenAI Gym and TensorFlow - Second Edition

Sudharsan Ravichandiran

With significant enhancements in the quality and quantity of algorithms in recent years, this second edition of Hands-On Reinforcement Learning with Python has been revamped into an example-rich guide to learning state-of-the-art reinforcement learning (RL) and deep RL algorithms with TensorFlow 2 and the OpenAI Gym toolkit.In addition to exploring RL basics and foundational concepts such as Bellman equation, Markov decision processes, and dynamic programming algorithms, this second edition dives deep into the full spectrum of value-based, policy-based, and actor-critic RL methods. It explores state-of-the-art algorithms such as DQN, TRPO, PPO and ACKTR, DDPG, TD3, and SAC in depth, demystifying the underlying math and demonstrating implementations through simple code examples.The book has several new chapters dedicated to new RL techniques, including distributional RL, imitation learning, inverse RL, and meta RL. You will learn to leverage stable baselines, an improvement of OpenAI’s baseline library, to effortlessly implement popular RL algorithms. The book concludes with an overview of promising approaches such as meta-learning and imagination augmented agents in research.By the end, you will become skilled in effectively employing RL and deep RL in your real-world projects.

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!