Adresat nieznany

Barbara Gordon

Ciche PRL-owskie miasteczko, nieśmiali mieszkańcy i zbrodnia doskonała... W cichym Lipowie, młody mężczyzna odkrywa, że śmierć jego bliskich jest połączona z historią lokalnej fabryki. Im bardziej próbuje rozgryźć te powiązania, tym bardziej czuje się zagrożony. Czy odważy się stawić czoła prawdzie, której strzegą cienie przeszłości i...lokalna piękność? Śledztwo zostaje przydzielone doświadczonemu kapitanowi Chmurze. Czy zmowa milczenia mieszkańców może okazać się tak silna, że prawdę odkryje zbyt późno? Znajdziecie tu nie tylko zagadki kryminalne, ale i wiernie odmalowaną rzeczywistość Polski w czasach PRL. Lubicie kryminały Leopolda Tyrmanda? Jeśli tak, to pozycja dla Was! PRL kryminalnie PRL kryminalnie - seria składająca się z powieści milicyjnych najbardziej poczytnych autorek i autorów czasów PRL. Barbara Gordon - właść. Larysa Zajączkowska-Mitznerowa, polska pisarka kryminałów PRL urodzona w Kijowie. Pisała również pod innymi pseudonimami, jest autorką około 24 książek, w tym powieści takich jak "Adresat nieznany" i "Błąd porucznika Kwaśniaka".

ADT w raku prostaty 2022 - wybór czy konieczność?

Artur Drobniak, Lesław Malik

Rak prostaty jest coraz częściej występującym nowotworem złośliwym. Terapia opiera się na postępowaniu miejscowym (chirurgia, radioterapia) i leczeniu systemowym (hormonoterapia, chemioterapia). Deprywacja androgenowa odgrywa kluczową rolę w leczeniu raka stercza. Różne kombinacje leków pozwalają na coraz dłuższy czas życia pacjentów. W artykule przedstawiono wybrane zagadnienia związane z nowymi możliwościami leczenia w różnych stadiach choroby.

Advanced Analytics with R and Tableau. Advanced analytics using data classification, unsupervised learning and data visualization

Ruben Oliva Ramos, Jen Stirrup, Roberto Rösler

Tableau and R offer accessible analytics by allowing a combination of easy-to-use data visualization along with industry-standard, robust statistical computation.Moving from data visualization into deeper, more advanced analytics? This book will intensify data skills for data viz-savvy users who want to move into analytics and data science in order to enhance their businesses by harnessing the analytical power of R and the stunning visualization capabilities of Tableau. Readers will come across a wide range of machine learning algorithms and learn how descriptive, prescriptive, predictive, and visually appealing analytical solutions can be designed with R and Tableau. In order to maximize learning, hands-on examples will ease the transition from being a data-savvy user to a data analyst using sound statistical tools to perform advanced analytics.By the end of this book, you will get to grips with advanced calculations in R and Tableau for analytics and prediction with the help of use cases and hands-on examples.

Advanced Blockchain Development. Build highly secure, decentralized applications and conduct secure transactions

Imran Bashir, Narayan Prusty

Blockchain technology is a distributed ledger with applications in industries such as finance, government, and media. This Learning Path is your guide to building blockchain networks using Ethereum, JavaScript, and Solidity. You will get started by understanding the technical foundations of blockchain technology, including distributed systems, cryptography and how this digital ledger keeps data secure. Further into the chapters, you’ll gain insights into developing applications using Ethereum and Hyperledger. As you build on your knowledge of Ether security, mining, smart contracts, and Solidity, you’ll learn how to create robust and secure applications that run exactly as programmed without being affected by fraud, censorship, or third-party interference. Toward the concluding chapters, you’ll explore how blockchain solutions can be implemented in applications such as IoT apps, in addition to its use in currencies. This Learning Path also highlights how you can increase blockchain scalability, and discusses the future scope of this fascinating and powerful technology. By the end of this Learning Path, you'll be equipped with the skills you need to tackle pain points encountered in the blockchain life cycle and confidently design and deploy decentralized applications.

Advanced C++. Master the technique of confidently writing robust C++ code

Gazihan Alankus, Olena Lizina, Rakesh Mane, Vivek Nagarajan, ...

C++ is one of the most widely used programming languages and is applied in a variety of domains, right from gaming to graphical user interface (GUI) programming and even operating systems. If you're looking to expand your career opportunities, mastering the advanced features of C++ is key.The book begins with advanced C++ concepts by helping you decipher the sophisticated C++ type system and understand how various stages of compilation convert source code to object code. You'll then learn how to recognize the tools that need to be used in order to control the flow of execution, capture data, and pass data around. By creating small models, you'll even discover how to use advanced lambdas and captures and express common API design patterns in C++. As you cover later chapters, you'll explore ways to optimize your code by learning about memory alignment, cache access, and the time a program takes to run. The concluding chapter will help you to maximize performance by understanding modern CPU branch prediction and how to make your code cache-friendly.By the end of this book, you'll have developed programming skills that will set you apart from other C++ programmers.

Advanced C++ Programming Cookbook. Become an expert C++ programmer by mastering concepts like templates, concurrency, and type deduction

Dr. Rian Quinn

If you think you've mastered C++ and know everything it takes to write robust applications, you'll be in for a surprise. With this book, you'll gain comprehensive insights into C++, covering exclusive tips and interesting techniques to enhance your app development process.You'll kick off with the basic principles of library design and development, which will help you understand how to write reusable and maintainable code. You'll then discover the importance of exception safety, and how you can avoid unexpected errors or bugs in your code. The book will take you through the modern elements of C++, such as move semantics, type deductions, and coroutines. As you advance, you'll delve into template programming - the standard tool for most library developers looking to achieve high code reusability. You'll explore the STL and learn how to avoid common pitfalls while implementing templates. Later, you'll learn about the problems of multithreaded programming such as data races, deadlocks, and thread starvation. You'll also learn high-performance programming by using benchmarking tools and libraries. Finally, you'll discover advanced techniques for debugging and testing to ensure code reliability.By the end of this book, you'll have become an expert at C++ programming and will have gained the skills to solve complex development problems with ease.

Advanced Deep Learning with Keras. Apply deep learning techniques, autoencoders, GANs, variational autoencoders, deep reinforcement learning, policy gradients, and more

Rowel Atienza

Recent developments in deep learning, including Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and Deep Reinforcement Learning (DRL) are creating impressive AI results in our news headlines - such as AlphaGo Zero beating world chess champions, and generative AI that can create art paintings that sell for over $400k because they are so human-like.Advanced Deep Learning with Keras is a comprehensive guide to the advanced deep learning techniques available today, so you can create your own cutting-edge AI. Using Keras as an open-source deep learning library, you'll find hands-on projects throughout that show you how to create more effective AI with the latest techniques.The journey begins with an overview of MLPs, CNNs, and RNNs, which are the building blocks for the more advanced techniques in the book. You’ll learn how to implement deep learning models with Keras and TensorFlow 1.x, and move forwards to advanced techniques, as you explore deep neural network architectures, including ResNet and DenseNet, and how to create autoencoders. You then learn all about GANs, and how they can open new levels of AI performance. Next, you’ll get up to speed with how VAEs are implemented, and you’ll see how GANs and VAEs have the generative power to synthesize data that can be extremely convincing to humans - a major stride forward for modern AI. To complete this set of advanced techniques, you'll learn how to implement DRL such as Deep Q-Learning and Policy Gradient Methods, which are critical to many modern results in AI.

Advanced Deep Learning with Python. Design and implement advanced next-generation AI solutions using TensorFlow and PyTorch

Ivan Vasilev

In order to build robust deep learning systems, you’ll need to understand everything from how neural networks work to training CNN models. In this book, you’ll discover newly developed deep learning models, methodologies used in the domain, and their implementation based on areas of application. You’ll start by understanding the building blocks and the math behind neural networks, and then move on to CNNs and their advanced applications in computer vision. You'll also learn to apply the most popular CNN architectures in object detection and image segmentation. Further on, you’ll focus on variational autoencoders and GANs. You’ll then use neural networks to extract sophisticated vector representations of words, before going on to cover various types of recurrent networks, such as LSTM and GRU. You’ll even explore the attention mechanism to process sequential data without the help of recurrent neural networks (RNNs). Later, you’ll use graph neural networks for processing structured data, along with covering meta-learning, which allows you to train neural networks with fewer training samples. Finally, you’ll understand how to apply deep learning to autonomous vehicles.By the end of this book, you’ll have mastered key deep learning concepts and the different applications of deep learning models in the real world.