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!

Accelerating Nonprofit Impact with Salesforce. Implement Nonprofit Cloud for efficient and cost-effective operations to drive your nonprofit mission

Melissa Hill Dees

Salesforce Nonprofit Cloud enables a 360-degree view of people related to your nonprofit to connect fundraising, program management, and grantmaking. With a single, unified view of every interaction with constituents, nonprofits can create strong relationships with the community and streamline internal processes.The book starts by covering the tools and features that make up Nonprofit Cloud, helping you understand their standard functionalities and how Nonprofit Success Pack's (NPSP) data architecture is critical to implementation. You’ll learn how the Nonprofit Cloud Program Management Module can connect your programs, automate case management, and track client progress. Next, you’ll explore the tools for creating a change management process to increase user adoption. Moving ahead, you’ll understand how to configure necessary permissions for NPSP administration and explore how declarative tools help better align the goals of a nonprofit organization. Toward the concluding chapters, you’ll cover customizations, deployment, custom reports, and dashboards for fundraising analytics, as well as best practices for data management to maintain its integrity.By the end of this Salesforce book, you’ll be able to build and configure the Nonprofit Cloud for a variety of use cases to achieve maximum social impact with the least amount of technical debt.

Access. Analiza danych. Receptury

Wayne Freeze, Ken Bluttman

Korzystaj z bazy danych Access jak profesjonalista! Jak stosować wskaźniki statystyczne do analizy danych biznesowych? Jak rozszerzać funkcjonalność zapytań SQL, stosując skrypty VBA? Jak przetwarzać dane i przenosić je między bazami Access? Access to znane już narzędzie służące do wszechstronnego przetwarzania i analizy danych. Posiada sporo ukrytych mechanizmów, pozwalających efektywnie wykonywać zadania, które początkowo mogą wydawać się skomplikowane. Książka przedstawia przykłady kwerend, metody przenoszenia danych pomiędzy bazami Access, obliczania wielu wskaźników finansowo-biznesowych i sporo innych zagadnień - wszystko pod kątem analizy i przetwarzania danych. Każda zaprezentowana receptura jest opatrzona kompletnym opisem rozwiązania problemu wraz ze szczegółowym omówieniem metody postępowania oraz analizą kodu. Access. Analiza danych. Receptury to uniwersalny podręcznik przeznaczony zarówno dla początkujących użytkowników bazy danych Access, jak i doświadczonych. Dzięki przejrzystemu językowi i mnogości poruszonych zagadnień każdy, niezależnie od stopnia zaawansowania, może poszerzyć swoją wiedzę. Zawiera mnóstwo ciekawych wskazówek i technik ułatwiających codzienną pracę z bazami danych, co czyni ją atrakcyjną nawet dla osób doskonale posługujących się bazą Access. Jest to także kompendium wiedzy niezbędnej każdemu, kto chce wyciągać ze zbiorów danych naprawdę cenne informacje. Tworzenie kwerend różnych typów Wstawianie, aktualizacja i usuwanie danych Przetwarzanie tekstu i liczb zapisanych w formie łańcucha znaków Zastosowanie tabel, modyfikacja zawartości systemu Windows, szyfrowanie danych Wykorzystanie obiektu FileSystemObject, przetwarzanie danych XML oraz XSLT, komunikacja z bazami SQL Rozwiązywanie problemów biznesowych Obliczanie wskaźników statystycznych Baza danych to fundament biznesu - zobacz, jak efektywnie nią zarządzać!

Active Machine Learning with Python. Refine and elevate data quality over quantity with active learning

Margaux Masson-Forsythe

Building accurate machine learning models requires quality data—lots of it. However, for most teams, assembling massive datasets is time-consuming, expensive, or downright impossible. Led by Margaux Masson-Forsythe, a seasoned ML engineer and advocate for surgical data science and climate AI advancements, this hands-on guide to active machine learning demonstrates how to train robust models with just a fraction of the data using Python's powerful active learning tools.You’ll master the fundamental techniques of active learning, such as membership query synthesis, stream-based sampling, and pool-based sampling and gain insights for designing and implementing active learning algorithms with query strategy and Human-in-the-Loop frameworks. Exploring various active machine learning techniques, you’ll learn how to enhance the performance of computer vision models like image classification, object detection, and semantic segmentation and delve into a machine AL method for selecting the most informative frames for labeling large videos, addressing duplicated data. You’ll also assess the effectiveness and efficiency of active machine learning systems through performance evaluation.By the end of the book, you’ll be able to enhance your active learning projects by leveraging Python libraries, frameworks, and commonly used tools.

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 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.

Advanced Deep Learning with R. Become an expert at designing, building, and improving advanced neural network models using R

Bharatendra Rai

Deep learning is a branch of machine learning based on a set of algorithms that attempt to model high-level abstractions in data. Advanced Deep Learning with R will help you understand popular deep learning architectures and their variants in R, along with providing real-life examples for them. This deep learning book starts by covering the essential deep learning techniques and concepts for prediction and classification. You will learn about neural networks, deep learning architectures, and the fundamentals for implementing deep learning with R. The book will also take you through using important deep learning libraries such as Keras-R and TensorFlow-R to implement deep learning algorithms within applications. You will get up to speed with artificial neural networks, recurrent neural networks, convolutional neural networks, long short-term memory networks, and more using advanced examples. Later, you'll discover how to apply generative adversarial networks (GANs) to generate new images; autoencoder neural networks for image dimension reduction, image de-noising and image correction and transfer learning to prepare, define, train, and model a deep neural network. By the end of this book, you will be ready to implement your knowledge and newly acquired skills for applying deep learning algorithms in R through real-world examples.