40 Algorithms Every Programmer Should Know. Hone your problem-solving skills by learning different algorithms and their implementation in Python

Imran Ahmad

Algorithms have always played an important role in both the science and practice of computing. Beyond traditional computing, the ability to use algorithms to solve real-world problems is an important skill that any developer or programmer must have. This book will help you not only to develop the skills to select and use an algorithm to solve real-world problems but also to understand how it works.You’ll start with an introduction to algorithms and discover various algorithm design techniques, before exploring how to implement different types of algorithms, such as searching and sorting, with the help of practical examples. As you advance to a more complex set of algorithms, you'll learn about linear programming, page ranking, and graphs, and even work with machine learning algorithms, understanding the math and logic behind them. Further on, case studies such as weather prediction, tweet clustering, and movie recommendation engines will show you how to apply these algorithms optimally. Finally, you’ll become well versed in techniques that enable parallel processing, giving you the ability to use these algorithms for compute-intensive tasks.By the end of this book, you'll have become adept at solving real-world computational problems by using a wide range of algorithms.

40 algorytmów, które powinien znać każdy programista. Nauka implementacji algorytmów w Pythonie

Imran Ahmad

Wiedza o algorytmach jest niezbędna każdemu, kto rozwiązuje problemy programistyczne. Algorytmy są również ważne w teorii i praktyce obliczeń. Każdy programista powinien znać możliwie szeroki ich zakres. Powinien też umieć z nich korzystać przy rozwiązywaniu rzeczywistych problemów, w tym przy projektowaniu algorytmów, ich modyfikacji i implementacji. Niezależnie od tego, czy zajmujesz się sztuczną inteligencją, zabezpieczaniem systemów informatycznych lub inżynierią danych, musisz dobrze zrozumieć, czym właściwie są i jak działają algorytmy. Ta książka jest praktycznym wprowadzeniem do algorytmów i ich zastosowania. Znalazły się w niej podstawowe informacje i pojęcia dotyczące algorytmów, ich działania, a także ograniczeń, jakim podlegają. Opisano też techniki ich projektowania z uwzględnieniem wymagań dotyczących struktur danych. Zaprezentowano klasyczne algorytmy sortowania i wyszukiwania, algorytmy grafowe, jak również wiele zagadnień związanych ze sztuczną inteligencją: algorytmy uczenia maszynowego, sieci neuronowych i przetwarzania języka naturalnego. Ważną częścią publikacji są rozdziały poświęcone przetwarzaniu danych i kryptografii oraz algorytmom powiązanym z tymi zagadnieniami. Wartościowym podsumowaniem prezentowanych treści jest omówienie technik pracy z problemami NP-trudnymi. W książce między innymi: struktury danych i algorytmy w bibliotekach Pythona algorytm grafowy służący do wykrywania oszustw w procesie analizy sieciowej przewidywanie pogody przy użyciu algorytmów uczenia nadzorowanego rozpoznawanie obrazu za pomocą syjamskich sieci neuronowych tworzenie systemu rekomendacji filmów szyfrowanie symetryczne i asymetryczne podczas wdrażania modelu uczenia maszynowego Oto algorytm: poznaj, zaimplementuj, zastosuj!

A Developer's Guide to .NET in Azure. Build quick, scalable cloud-native applications and microservices with .NET 6.0 and Azure

Anuraj Parameswaran, Tamir Al Balkhi

A Developer’s Guide to .NET in Azure helps you embark on a transformative journey through Microsoft Azure that is tailored to .NET developers. This book is a curated compendium that’ll enable you to master the creation of resilient, scalable, and highly available applications.The book is divided into four parts, with Part 1 demystifying Azure for you and emphasizing the portal's utility and seamless integration. The chapters in this section help you configure your workspace for optimal Azure synergy. You’ll then move on to Part 2, where you’ll explore serverless computing, microservices, containerization, Dapr, and Azure Kubernetes Service for scalability, and build pragmatic, cost-effective applications using Azure Functions and Container apps. Part 3 delves into data and storage, showing you how to utilize Azure Blob Storage for unstructured data, Azure SQL Database for structured data, and Azure Cosmos DB for document-oriented data. The final part teaches you about messaging and security, utilizing Azure App Configuration, Event Hubs, Service Bus, Key Vault, and Azure AD B2C for robust, secure applications.By the end of this book, you’ll have mastered Azure's responsive infrastructure for exceptional applications.

Accelerate Model Training with PyTorch 2.X. Build more accurate models by boosting the model training process

Maicon Melo Alves, Lúcia Maria de Assumpçao Drummond

Penned by an expert in High-Performance Computing (HPC) with over 25 years of experience, this book is your guide to enhancing the performance of model training using PyTorch, one of the most widely adopted machine learning frameworks.You’ll start by understanding how model complexity impacts training time before discovering distinct levels of performance tuning to expedite the training process. You’ll also learn how to use a new PyTorch feature to compile the model and train it faster, alongside learning how to benefit from specialized libraries to optimize the training process on the CPU. As you progress, you’ll gain insights into building an efficient data pipeline to keep accelerators occupied during the entire training execution and explore strategies for reducing model complexity and adopting mixed precision to minimize computing time and memory consumption. The book will get you acquainted with distributed training and show you how to use PyTorch to harness the computing power of multicore systems and multi-GPU environments available on single or multiple machines. By the end of this book, you’ll be equipped with a suite of techniques, approaches, and strategies to speed up training , so you can focus on what really matters—building stunning models!

Actionable Insights with Amazon QuickSight. Develop stunning data visualizations and machine learning-driven insights with Amazon QuickSight

Manos Samatas

Amazon Quicksight is an exciting new visualization that rivals PowerBI and Tableau, bringing several exciting features to the table – but sadly, there aren’t many resources out there that can help you learn the ropes. This book seeks to remedy that with the help of an AWS-certified expert who will help you leverage its full capabilities.After learning QuickSight’s fundamental concepts and how to configure data sources, you’ll be introduced to the main analysis-building functionality of QuickSight to develop visuals and dashboards, and explore how to develop and share interactive dashboards with parameters and on-screen controls. You’ll dive into advanced filtering options with URL actions before learning how to set up alerts and scheduled reports. Next, you’ll familiarize yourself with the types of insights before getting to grips with adding ML insights such as forecasting capabilities, analyzing time series data, adding narratives, and outlier detection to your dashboards. You’ll also explore patterns to automate operations and look closer into the API actions that allow us to control settings. Finally, you’ll learn advanced topics such as embedded dashboards and multitenancy.By the end of this book, you’ll be well-versed with QuickSight’s BI and analytics functionalities that will help you create BI apps with ML capabilities.

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.

Advanced Deep Learning with TensorFlow 2 and Keras. Apply DL, GANs, VAEs, deep RL, unsupervised learning, object detection and segmentation, and more - Second Edition

Rowel Atienza

Advanced Deep Learning with TensorFlow 2 and Keras, Second Edition is a completely updated edition of the bestselling guide to the advanced deep learning techniques available today. Revised for TensorFlow 2.x, this edition introduces you to the practical side of deep learning with new chapters on unsupervised learning using mutual information, object detection (SSD), and semantic segmentation (FCN and PSPNet), further allowing you to create your own cutting-edge AI projects.Using Keras as an open-source deep learning library, the book features hands-on projects that show you how to create more effective AI with the most up-to-date techniques.Starting with an overview of multi-layer perceptrons (MLPs), convolutional neural networks (CNNs), and recurrent neural networks (RNNs), the book then introduces more cutting-edge techniques as you explore deep neural network architectures, including ResNet and DenseNet, and how to create autoencoders. You will then learn about GANs, and how they can unlock new levels of AI performance.Next, you’ll discover how a variational autoencoder (VAE) is implemented, and how GANs and VAEs have the generative power to synthesize data that can be extremely convincing to humans. You'll also learn to implement DRL such as Deep Q-Learning and Policy Gradient Methods, which are critical to many modern results in AI.

Advanced Python Programming. Accelerate your Python programs using proven techniques and design patterns - Second Edition

Quan Nguyen

Python's powerful capabilities for implementing robust and efficient programs make it one of the most sought-after programming languages.In this book, you'll explore the tools that allow you to improve performance and take your Python programs to the next level.This book starts by examining the built-in as well as external libraries that streamline tasks in the development cycle, such as benchmarking, profiling, and optimizing. You'll then get to grips with using specialized tools such as dedicated libraries and compilers to increase your performance at number-crunching tasks, including training machine learning models.The book covers concurrency, a major solution to making programs more efficient and scalable, and various concurrent programming techniques such as multithreading, multiprocessing, and asynchronous programming.You'll also understand the common problems that cause undesirable behavior in concurrent programs.Finally, you'll work with a wide range of design patterns, including creational, structural, and behavioral patterns that enable you to tackle complex design and architecture challenges, making your programs more robust and maintainable.By the end of the book, you'll be exposed to a wide range of advanced functionalities in Python and be equipped with the practical knowledge needed to apply them to your use cases.