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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

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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 - okladka książki

Autorzy:
Matt Benatan, Jochem Gietema, Marian Schneider
Wydawnictwo:
Packt Publishing (Z chęcią przeczytam książkę w języku polskim)
Ocena:
Bądź pierwszym, który oceni tę książkę
Stron:
386
Dostępne formaty:
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     ePub

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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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