Enhancing Deep Learning Bayesian by Benatan Matt (12 results)

Paperback or Softback. Condition: New. Enhancing Deep Learning with Bayesian Inference: Create more powerful, robust deep learning systems with Bayesian deep learning in Python 1.46. Book.

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Paperback. Condition: New. Develop Bayesian Deep Learning models to help make your own applications more robust.Key FeaturesGain insights into the limitations of typical neural networksAcquire the skill to cultivate neural networks capable of estimating uncertaintyDiscover how to leverage uncertainty to develop more robust machine learning systemsBook DescriptionDeep 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.What you will learnUnderstand advantages and disadvantages of Bayesian inference and deep learningUnderstand the fundamentals of Bayesian Neural NetworksUnderstand the differences between key BNN implementations/approximationsUnderstand the advantages of probabilistic DNNs in production contextsHow to implement a variety of BDL methods in Python codeHow to apply BDL methods to real-world problemsUnderstand how to evaluate BDL methods and choose the best method for a given taskLearn how to deal with unexpected data in real-world deep learning applicationsWho this book is forThis book will cater to researchers and developers looking for ways to develop more robust deep learning models through probabilistic deep learning. You're 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.

Condition: New. pp. 386.

Paperback. Condition: New. Develop Bayesian Deep Learning models to help make your own applications more robust.Key FeaturesGain insights into the limitations of typical neural networksAcquire the skill to cultivate neural networks capable of estimating uncertaintyDiscover how to leverage uncertainty to develop more robust machine learning systemsBook DescriptionDeep 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.What you will learnUnderstand advantages and disadvantages of Bayesian inference and deep learningUnderstand the fundamentals of Bayesian Neural NetworksUnderstand the differences between key BNN implementations/approximationsUnderstand the advantages of probabilistic DNNs in production contextsHow to implement a variety of BDL methods in Python codeHow to apply BDL methods to real-world problemsUnderstand how to evaluate BDL methods and choose the best method for a given taskLearn how to deal with unexpected data in real-world deep learning applicationsWho this book is forThis book will cater to researchers and developers looking for ways to develop more robust deep learning models through probabilistic deep learning. You're 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.

PAP. Condition: New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

PAP. Condition: New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

Condition: New. Print on Demand pp. 386.

Condition: New. PRINT ON DEMAND pp. 386.

Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Develop Bayesian Deep Learning models to help make your own applications more robust.Key Features:Gain insights into the limitations of typical neural networksAcquire the skill to cultivate neural networks capable of estimating uncertaintyDiscover how to leverage uncertainty to develop more robust machine learning systemsBook Description: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.What You Will Learn:Discern the advantages and disadvantages of Bayesian inference and deep learningBecome well-versed with the fundamentals of Bayesian Neural NetworksUnderstand the differences between key BNN implementations and approximationsRecognize the merits of probabilistic DNNs in production contextsMaster the implementation of a variety of BDL methods in Python codeApply BDL methods to real-world problemsEvaluate BDL methods and choose the most suitable approach for a given taskDevelop proficiency in dealing with unexpected data in deep learning applicationsWho this book is for:This book will cater to researchers and developers looking for ways to develop more robust deep learning models through probabilistic deep learning. You're 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.