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Feature Engineering Bookcamp
Deliver huge improvements to your machine learning pipelines without spending hours fine-tuning parameters! This book’s practical case-studies reveal feature engineering techniques that upgrade your data wrangling—and your ML results. In Feature Engineering Bookcamp you will learn how to: Identify and implement feature transformations for your data Build powerful machine learning pipelines with unstructured data like text and images Quantify and minimize bias in machine learning pipelines at the data level Use feature stores to build real-time feature engineering pipelines Enhance existing machine learning pipelines by manipulating the input data Use state-of-the-art deep learning models...
MLOps Engineering at Scale
Dodge costly and time-consuming infrastructure tasks, and rapidly bring your machine learning models to production with MLOps and pre-built serverless tools! In MLOps Engineering at Scale you will learn: Extracting, transforming, and loading datasets Querying datasets with SQL Understanding automatic differentiation in PyTorch Deploying model training pipelines as a service endpoint Monitoring and managing your pipeline’s life cycle Measuring performance improvements MLOps Engineering at Scale shows you how to put machine learning into production efficiently by using pre-built services from AWS and other cloud vendors. You’ll learn how to rapidly create flexible and scalable machine lear...
PyTorch kompakt
Eine großartige Ressource für alle, die mit PyTorch arbeiten Kurzgefasstes und präzises Wissen zu dem populären Deep-Learning-Framework Sowohl für PyTorch-Einsteiger:innen als auch für Fortgeschrittene nützlich Überblick über Modellentwicklung, Deployment, das PyTorch-Ökosystem und über hilfreiche PyTorch-Bibliotheken Mit Kurzeinstieg in PyTorch Mit diesem benutzerfreundlichen Nachschlagewerk zu PyTorch haben Sie kompaktes Wissen zu einem der beliebtesten Frameworks für Deep Learning immer zur Hand. Der Autor Joe Papa bietet Ihnen mit dieser Referenz den sofortigen Zugriff auf Syntax, Design Patterns und gut nachvollziehbare Codebeispiele - eine Fülle an gesammelten Informatione...
PyTorch Pocket Reference
This concise, easy-to-use reference puts one of the most popular frameworks for deep learning research and development at your fingertips. Author Joe Papa provides instant access to syntax, design patterns, and code examples to accelerate your development and reduce the time you spend searching for answers. Research scientists, machine learning engineers, and software developers will find clear, structured PyTorch code that covers every step of neural network development-from loading data to customizing training loops to model optimization and GPU/TPU acceleration. Quickly learn how to deploy your code to production using AWS, Google Cloud, or Azure and deploy your ML models to mobile and edge devices. Learn basic PyTorch syntax and design patterns Create custom models and data transforms Train and deploy models using a GPU and TPU Train and test a deep learning classifier Accelerate training using optimization and distributed training Access useful PyTorch libraries and the PyTorch ecosystem
Serverless Architectures on AWS, Second Edition
Design low-maintenance systems using pre-built cloud services! Bring down costs, automate time-consuming ops tasks, and scale on demand. In Serverless Architectures on AWS, Second Edition you will learn: First steps with serverless computing The principles of serverless design Important patterns and architectures How successfully companies have implemented serverless Real-world architectures and their tradeoffs Serverless Architectures on AWS, Second Edition teaches you how to design serverless systems. You’ll discover the principles behind serverless architectures, and explore real-world case studies where companies used serverless architectures for their products. You won’t just master...
KI in der Projektwirtschaft 2
Dieser Band zeigt, wie KI die Projektwirtschaft neugestaltet werden kann und welche Herausforderungen in Projekten sich damit innovativ und effizient meistern lassen. Mit den Schwerpunktthemen KI-Einsatz in konkreten Anwendungen, in der Forschung zu KI Start-ups für Projektmanagement, Veränderung der Rollen in den Unternehmen durch KI sowie Portfolio und Mustererkennung wird der Fokus auf konkrete praxisnahe Anwendung gelegt. Es wird dargelegt, wie erfolgreiche Umsetzungen aus der Forschung in die Anwendung durch Start-ups gelingen können. Darüber hinaus entstehen mit der Einführung von KI in die Projektwirtschaft neue Berufsbilder und Rollen, wie z.B. Datenanalyst:innen für Projekte, KI-Strateg:innen oder spezialisierte KI-Trainer:innen, die eine Neuausrichtung der Aus- und Weiterbildung und Anpassungen in der Unternehmenskultur erfordern. Als innovativer Anwendungsbereich von KI in der Projektwirtschaft wird der Bereich "Early Warning Systems" gesehen, indem mittels Mustererkennung gezieltes präventives Handeln ermöglicht und damit bessere Projektergebnisse erzielt werden können.
The Red Jester
What was Andrei Bely's aim in his ambiguous novel Petersburg? For the first time, this study firmly places Bely's work at the heart of the European Modern (die Moderne). The book argues that the novel - with its concern for the spiritual and its desire to create new aesthetics - helped reshape fundamental views of reality, of the Self, and of consciousness. Theories of Freud and Jung, as well as the aesthetics of the Viennese Secession, are used to elucidate Bely's approach to the narrative. The book also presents Rudolf Steiner's anthroposophy as the prism through which Bely reflects modernist ideas. (Series: Slavistik - Vol. 1)
Feeling Revolution
Feeling Revolution explores the important role played by film genres in cultivating the Stalin era's distinctive emotional values and norms -- ranging from happiness to hatred for enemies. Toropova's exploration of a wide variety of primary sources brings to light the Soviet film industry's battle to shape new forms of audience response.
