Articoli correlati a Deep Learning Crash Course
Sinossi
This comprehensive, hands-on guide to deep learning with Python covers fundamental concepts and advanced techniques to apply deep neural network models in real-world scenarios.
Deep Learning Crash Course starts from the basics to explore the most modern techniques and applications that are of great interest right now, and whose popularity will only grow in the future. It covers advanced topics such as generative models (the technology behind deep fakes), self-supervised learning, attention mechanisms (the technology behind ChatGPT), diffusion models (the technology behind text2image models such as DALL-E), graph neural networks (the technology behind AlphaFold), and deep reinforcement learning (the technology behind AlphaGo). These cutting-edge concepts and techniques address the current demands and trends in deep learning, giving you practical skills to tackle complex real-world problems.
Le informazioni nella sezione "Riassunto" possono far riferimento a edizioni diverse di questo titolo.
Informazioni sull'autore
Giovanni Volpe, head of the Soft Matter Lab at the University of Gothenburg and recipient of the Göran Gustafsson Prize in Physics, has published extensively on deep learning and physics and developed key software packages including DeepTrack, Deeplay, and BRAPH. Benjamin Midtvedt and Jesús Pineda are core developers of DeepTrack and Deeplay. Henrik Klein Moberg and Harshith Bachimanchi apply AI to nanoscience and holographic microscopy. Joana B. Pereira, head of the Brain Connectomics Lab at the Karolinska Institute, organizes the annual conference Emerging Topics in Artificial Intelligence. Carlo Manzo, head of the Quantitative Bioimaging Lab at the University of Vic, is the founder of the Anomalous Diffusion Challenge.
Le informazioni nella sezione "Su questo libro" possono far riferimento a edizioni diverse di questo titolo.
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Deep Learning Crash Course
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Deep Learning Crash Course
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Paperback. Condizione: new. Paperback. A complete guide to deep neural networks - the technology behind AI - covering fundamental and advanced techniques to apply machine learning in real-world scenarios.Build AI Models from Scratch (No PhD Required)Deep Learning Crash Course is a fast-paced, thorough introduction that will have you building today's most powerful AI models from scratch. No experience with deep learning required!Designed for programmers who may be new to deep learning, this book offers practical, hands-on experience, not just an abstract understanding of theory.You'll start from the basics, and using PyTorch with real datasets, you'll quickly progress from your first neural network to advanced architectures like convolutional neural networks (CNNs), transformers, diffusion models, and graph neural networks (GNNs). Each project can be run on your own hardware or in the cloud, with annotated code available on GitHub.You'll build and train models to:Classify and analyze images, sequences, and time seriesGenerate and transform data with autoencoders, GANs (generative adversarial networks), and diffusion modelsProcess natural language with recurrent neural networks and transformersModel molecules and physical systems with graph neural networksImprove continuously through reinforcement and active learningPredict chaotic systems with reservoir computingWhether you're an engineer, scientist, or professional developer, you'll gain fluency in deep learning and the confidence to apply it to ambitious, real-world problems. With Deep Learning Crash Course, you'll move from using AI tools to creating them. "A comprehensive, hands-on guide to deep learning using Python, combining theoretical concepts with practical examples and step-by-step code implementation. Covers foundational topics as well as advanced subjects such as generative models and reinforcement learning"-- Provided by publisher. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Codice articolo 9781718503922
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Deep Learning Crash Course
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Paperback. Condizione: New. Deep Learning Crash Course goes beyond the basics of machine learning to delve into modern techniques and applications of great interest right now, and whose popularity will only grow in the future. The book covers topics such as generative models (the technology behind deep fakes), self-supervised learning, attention mechanisms (the tech behind ChatGPT), graph neural networks (the tech behind AlphaFold), and deep reinforcement learning (the tech behind AlphaGo). This book bridges the gap between theory and practice, helping readers gain the confidence to apply deep learning in their work. Codice articolo LU-9781718503922
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