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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

(ebook) (audiobook) (audiobook) Język publikacji: angielski
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

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 MP3

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

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:
     PDF
     ePub
Ebook
161,10 zł 179,00 zł (-10%)
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Deep learning has an increasingly significant impact on our lives, from suggesting content to playing a key role in mission- and safety-critical applications. As the influence of these algorithms grows, so does the concern for the safety and robustness of the systems which rely on them. Simply put, typical deep learning methods do not know when they don’t know.
The field of Bayesian Deep Learning contains a range of methods for approximate Bayesian inference with deep networks. These methods help to improve the robustness of deep learning systems as they tell us how confident they are in their predictions, allowing us to take more in how we incorporate model predictions within our applications.
Through this book, you will be introduced to the rapidly growing field of uncertainty-aware deep learning, developing an understanding of the importance of uncertainty estimation in robust machine learning systems. You will learn about a variety of popular Bayesian Deep Learning methods, and how to implement these through practical Python examples covering a range of application scenarios.
By the end of the book, you will have a good understanding of Bayesian Deep Learning and its advantages, and you will be able to develop Bayesian Deep Learning 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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