Enhancing Deep Learning with Bayesian Inference. Create more powerful, robust deep learning systems with Bayesian deep learning in Python

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Autorzy:: Matt Benatan, Jochem Gietema, Marian Schneider

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Enhancing Deep Learning with Bayesian Inference. Create more powerful, robust deep learning systems with Bayesian deep learning in Python Matt Benatan, Jochem Gietema, Marian Schneider - okladka książki

Enhancing Deep Learning with Bayesian Inference. Create more powerful, robust deep learning systems with Bayesian deep learning in Python Matt Benatan, Jochem Gietema, Marian Schneider - audiobook CD

Wydawnictwo:: Packt Publishing (Z chęcią przeczytam książkę w języku polskim)
Ocena:: Bądź pierwszym, który oceni tę książkę
Stron:: 386
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Do przechowalni

Deep learning is revolutionizing our lives, impacting content recommendations and playing a key role in mission- and safety-critical applications. Yet, typical deep learning methods lack awareness about uncertainty. Bayesian deep learning offers solutions based on approximate Bayesian inference, enhancing the robustness of deep learning systems by indicating how confident they are in their predictions. This book will guide you in incorporating model predictions within your applications with care.
Starting with an introduction to the rapidly growing field of uncertainty-aware deep learning, you’ll discover the importance of uncertainty estimation in robust machine learning systems. You’ll then explore a variety of popular Bayesian deep learning methods and understand how to implement them through practical Python examples covering a range of application scenarios.
By the end of this book, you’ll embrace the power of Bayesian deep learning and unlock a new level of confidence in your models for safer, more robust deep learning systems.

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O autorach książki

Dr. Matt Benatan is a research scientist in Machine Learning and AI in Sonos's Context Awareness research group. I have significant expertise in developing and applying Machine Learning techniques across a variety of domains, including Audio DSP, Speech Processing, and Computer Vision. I have also worked extensively on the development and application of Bayesian methods, including Bayesian optimisation and scalable Bayesian inference (with a focus on Bayesian neural networks). Collaborator and PhD co-supervisor on a couple of research projects with the University of Manchester.
Graduate of the University of Leeds with a PhD in Audio-Visual Speech Processing (Computer Science), Master's degree in Computer Science and Electronics (MSc by Research) and a BSc (Hons) degree in Music, Multimedia and Electronics.

Jochem Gietema is a Machine Learning Research Engineer at Onfido. He is experienced in developing and deploying machine learning models to solve real-world problems. He has a strong background in computer vision and natural language processing, with a focus on deep learning techniques. Jochem is passionate about advancing the state-of-the-art in machine learning and helping organizations to use it to improve their operations

Marian Schneider is a skilled Machine Learning Research Engineer who specializes in utilizing cutting-edge technology to solve real-world problems. She has extensive experience in various areas of machine learning including computer vision, natural language processing, and deep learning. Marian is dedicated to advancing the state-of-the-art in machine learning and is committed to helping organizations improve their operations by leveraging this technology.

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Szczegóły książki

Tytuł oryginału:: Enhancing Deep Learning with Bayesian Inference. Create more powerful, robust deep learning systems with Bayesian deep learning in Python
ISBN Ebooka:: 978-18-032-3725-1, 9781803237251
Data wydania ebooka :: 2023-06-30 Data wydania ebooka często jest dniem wprowadzenia tytułu do sprzedaży i może nie być równoznaczna z datą wydania książki papierowej. Dodatkowe informacje możesz znaleźć w darmowym fragmencie. Jeśli masz wątpliwości skontaktuj się z nami sklep@helion.pl.
Język publikacji:: angielski
Rozmiar pliku Pdf:: 17.1MB
Rozmiar pliku ePub:: 16.4MB

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Spis treści książki

1. Bayesian Inference in the Age of Deep Learning
2. Fundamentals of Bayesian Inference
3. Fundamentals of Deep Learning
4. Introducing Bayesian Deep Learning
5. Principled Approaches for Bayesian Deep Learning
6. Using the Standard Toolbox for Bayesian Deep Learning
7. Practical considerations for Bayesian Deep Learning
8. Applying Bayesian Deep Learning
9. Next Steps in Bayesian Deep Learning

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