Enhancing Deep Learning with Bayesian Inference

Name: Enhancing Deep Learning with Bayesian Inference
Brand: Packt Publishing
SKU: 01771663

Cargando comentarios…

$985

$886

Envío gratis siempre

Comentarios

Cargando el resumen…

Por favor, inicia sesión para escribir un comentario.

Cargando comentarios…

1798199Enhancing Deep Learning with Bayesian Inferencehttps://www.gandhi.com.mx/bayesian-deep-learning/phttps://gandhi.vtexassets.com/arquivos/ids/159531/02136b6c-8bc5-4169-9b11-4ba05259cbb4.jpg?v=638333635304830000886985MXNPackt PublishingInStock/Ebooks/1771663Enhancing Deep Learning with Bayesian Inference886985https://www.gandhi.com.mx/bayesian-deep-learning/phttps://gandhi.vtexassets.com/arquivos/ids/159531/02136b6c-8bc5-4169-9b11-4ba05259cbb4.jpg?v=638333635304830000InStockMXN99999DIEbook20239781803237251_W3siaWQiOiIzZjZlNTY1NC03N2JmLTQ1MTUtOWUyMy01ZmY0ZWVkOWY0ZmMiLCJsaXN0UHJpY2UiOjk4NSwiZGlzY291bnQiOjk5LCJzZWxsaW5nUHJpY2UiOjg4NiwiaW5jbHVkZXNUYXgiOnRydWUsInByaWNlVHlwZSI6Ildob2xlc2FsZSIsImN1cnJlbmN5IjoiTVhOIiwiZnJvbSI6IjIwMjQtMDQtMDhUMTY6MDA6MDBaIiwicmVnaW9uIjoiTVgiLCJpc1ByZW9yZGVyIjpmYWxzZX1d9781803237251_<p><strong>Develop Bayesian Deep Learning models to help make your own applications more robust.</strong></p><h4>Key Features</h4><ul><li>Learn how advanced convolutions work</li><li>Learn to implement a convolution neural network</li><li>Learn advanced architectures using convolution neural networks</li><li>Apply Bayesian NN to decrease weighted distribution</li></ul><h4>Book Description</h4><p>Bayesian Deep Learning provides principled methods for developing deep learning models capable of producing uncertainty estimates.</p><p>Typical deep learning methods do not produce principled uncertainty estimates, i.e. they dont know when they dont know. Principled uncertainty estimates allow developers to handle unexpected scenarios in real-world applications, and therefore facilitate the development of safer, more robust systems.</p><p>Developers working with deep learning will be able to put their knowledge to work with this practical guide to Bayesian Deep Learning.</p><p>Learn building and understanding of how Bayesian Deep Learning can improve the way you work with models in production.</p><p>Youll learn about the importance of uncertainty estimates in predictive tasks, and will be introduced to a variety of Bayesian Deep Learning approaches used to produce principled uncertainty estimates. You will be guided through the implementation of these approaches, and will learn how to select and apply Bayesian Deep Learning methods to real-world applications.</p><p>By the end of the book you will have a good understanding of Bayesian Deep Learning and the advantages it has to offer, and will be able to develop Bayesian Deep Learning models to help make your own applications more robust.</p><h4>What you will learn</h4><ul><li>Understanding the fundamentals of Bayesian Neural Networks</li><li>Understanding the tradeoffs between different key BNN implementations/approximations</li><li>Understanding the advantages of probabilistic DNNs in production contexts</li><li>Knowing how to implement a variety of BDL methods, and how to apply these to real-world problems</li><li>Understanding how to evaluate BDL methods and choose the best method for a given task</li></ul><h4>Who This Book Is For</h4><p>Researchers and developers are looking for ways to develop more robust deep learning models through probabilistic deep learning.</p><p>The reader will know the fundamentals of machine learning, and have some experience of working with machine learning and deep learning models.</p><h4>Table of Contents</h4><ol><li>Bayesian Inference in the Age of Deep Learning</li><li>Fundamentals of Bayesian Inference</li><li>Fundamentals of Deep Learning</li><li>Introducing Bayesian Deep Learning</li><li>Principled Approaches for Bayesian Deep Learning</li><li>Using the Standard Toolbox for Bayesian Deep Learning</li><li>Practical considerations for Bayesian Deep Learning</li><li>Applying Bayesian Deep Learning</li><li>Next steps in Bayesian Deep Learning</li></ol>...(*_*)9781803237251_<p><b>Develop Bayesian Deep Learning models to help make your own applications more robust.</b></p><h2>Key Features</h2><ul><li>Gain insights into the limitations of typical neural networks</li><li>Acquire the skill to cultivate neural networks capable of estimating uncertainty</li><li>Discover how to leverage uncertainty to develop more robust machine learning systems</li></ul><h2>Book Description</h2>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 dont 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.<h2>What you will learn</h2><ul><li>Understand advantages and disadvantages of Bayesian inference and deep learning</li><li>Understand the fundamentals of Bayesian Neural Networks</li><li>Understand the differences between key BNN implementations/approximations</li><li>Understand the advantages of probabilistic DNNs in production contexts</li><li>How to implement a variety of BDL methods in Python code</li><li>How to apply BDL methods to real-world problems</li><li>Understand how to evaluate BDL methods and choose the best method for a given task</li><li>Learn how to deal with unexpected data in real-world deep learning applications</li></ul><h2>Who this book is for</h2><p>This book will cater to researchers and developers looking for ways to develop more robust deep learning models through probabilistic deep learning. Youre expected to have a solid understanding of the fundamentals of machine learning and probability, along with prior experience working with machine learning and deep learning models.</p>...9781803237251_Packt Publishinglibro_electonico_38017ad8-6439-3715-ae44-2535157e5f1c_9781803237251;9781803237251_9781803237251Marian SchneiderInglésMéxicohttps://getbook.kobo.com/koboid-prod-public/packt-epub-4c9102c1-528b-45bd-92d0-c06f1a122460.epub2023-06-30T00:00:00+00:00Packt Publishing