Probabilistic Deep Learning
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Probabilistic Deep Learning
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Author : Beate Sick
language : en
Publisher: Simon and Schuster
Release Date : 2020-10-11
Probabilistic Deep Learning written by Beate Sick and has been published by Simon and Schuster this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-10-11 with Computers categories.
Probabilistic Deep Learning is a hands-on guide to the principles that support neural networks. Learn to improve network performance with the right distribution for different data types, and discover Bayesian variants that can state their own uncertainty to increase accuracy. This book provides easy-to-apply code and uses popular frameworks to keep you focused on practical applications. Summary Probabilistic Deep Learning: With Python, Keras and TensorFlow Probability teaches the increasingly popular probabilistic approach to deep learning that allows you to refine your results more quickly and accurately without much trial-and-error testing. Emphasizing practical techniques that use the Python-based Tensorflow Probability Framework, you’ll learn to build highly-performant deep learning applications that can reliably handle the noise and uncertainty of real-world data. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology The world is a noisy and uncertain place. Probabilistic deep learning models capture that noise and uncertainty, pulling it into real-world scenarios. Crucial for self-driving cars and scientific testing, these techniques help deep learning engineers assess the accuracy of their results, spot errors, and improve their understanding of how algorithms work. About the book Probabilistic Deep Learning is a hands-on guide to the principles that support neural networks. Learn to improve network performance with the right distribution for different data types, and discover Bayesian variants that can state their own uncertainty to increase accuracy. This book provides easy-to-apply code and uses popular frameworks to keep you focused on practical applications. What's inside Explore maximum likelihood and the statistical basis of deep learning Discover probabilistic models that can indicate possible outcomes Learn to use normalizing flows for modeling and generating complex distributions Use Bayesian neural networks to access the uncertainty in the model About the reader For experienced machine learning developers. About the author Oliver Dürr is a professor at the University of Applied Sciences in Konstanz, Germany. Beate Sick holds a chair for applied statistics at ZHAW and works as a researcher and lecturer at the University of Zurich. Elvis Murina is a data scientist. Table of Contents PART 1 - BASICS OF DEEP LEARNING 1 Introduction to probabilistic deep learning 2 Neural network architectures 3 Principles of curve fitting PART 2 - MAXIMUM LIKELIHOOD APPROACHES FOR PROBABILISTIC DL MODELS 4 Building loss functions with the likelihood approach 5 Probabilistic deep learning models with TensorFlow Probability 6 Probabilistic deep learning models in the wild PART 3 - BAYESIAN APPROACHES FOR PROBABILISTIC DL MODELS 7 Bayesian learning 8 Bayesian neural networks
Probabilistic Machine Learning
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Author : Kevin P. Murphy
language : en
Publisher: MIT Press
Release Date : 2022-03-01
Probabilistic Machine Learning written by Kevin P. Murphy and has been published by MIT Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-03-01 with Computers categories.
A detailed and up-to-date introduction to machine learning, presented through the unifying lens of probabilistic modeling and Bayesian decision theory. This book offers a detailed and up-to-date introduction to machine learning (including deep learning) through the unifying lens of probabilistic modeling and Bayesian decision theory. The book covers mathematical background (including linear algebra and optimization), basic supervised learning (including linear and logistic regression and deep neural networks), as well as more advanced topics (including transfer learning and unsupervised learning). End-of-chapter exercises allow students to apply what they have learned, and an appendix covers notation. Probabilistic Machine Learning grew out of the author’s 2012 book, Machine Learning: A Probabilistic Perspective. More than just a simple update, this is a completely new book that reflects the dramatic developments in the field since 2012, most notably deep learning. In addition, the new book is accompanied by online Python code, using libraries such as scikit-learn, JAX, PyTorch, and Tensorflow, which can be used to reproduce nearly all the figures; this code can be run inside a web browser using cloud-based notebooks, and provides a practical complement to the theoretical topics discussed in the book. This introductory text will be followed by a sequel that covers more advanced topics, taking the same probabilistic approach.
Probabilistic Machine Learning
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Author : Kevin P. Murphy
language : en
Publisher: MIT Press
Release Date : 2023-08-15
Probabilistic Machine Learning written by Kevin P. Murphy and has been published by MIT Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-08-15 with Computers categories.
An advanced book for researchers and graduate students working in machine learning and statistics who want to learn about deep learning, Bayesian inference, generative models, and decision making under uncertainty. An advanced counterpart to Probabilistic Machine Learning: An Introduction, this high-level textbook provides researchers and graduate students detailed coverage of cutting-edge topics in machine learning, including deep generative modeling, graphical models, Bayesian inference, reinforcement learning, and causality. This volume puts deep learning into a larger statistical context and unifies approaches based on deep learning with ones based on probabilistic modeling and inference. With contributions from top scientists and domain experts from places such as Google, DeepMind, Amazon, Purdue University, NYU, and the University of Washington, this rigorous book is essential to understanding the vital issues in machine learning. Covers generation of high dimensional outputs, such as images, text, and graphs Discusses methods for discovering insights about data, based on latent variable models Considers training and testing under different distributions Explores how to use probabilistic models and inference for causal inference and decision making Features online Python code accompaniment
Advances In Probabilistic Deep Learning
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Author : Raza Habib
language : en
Publisher:
Release Date : 2022
Advances In Probabilistic Deep Learning written by Raza Habib and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022 with categories.
Machine Learning
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Author : Kevin P. Murphy
language : en
Publisher: MIT Press
Release Date : 2012-09-07
Machine Learning written by Kevin P. Murphy and has been published by MIT Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-09-07 with Computers categories.
A comprehensive introduction to machine learning that uses probabilistic models and inference as a unifying approach. Today's Web-enabled deluge of electronic data calls for automated methods of data analysis. Machine learning provides these, developing methods that can automatically detect patterns in data and then use the uncovered patterns to predict future data. This textbook offers a comprehensive and self-contained introduction to the field of machine learning, based on a unified, probabilistic approach. The coverage combines breadth and depth, offering necessary background material on such topics as probability, optimization, and linear algebra as well as discussion of recent developments in the field, including conditional random fields, L1 regularization, and deep learning. The book is written in an informal, accessible style, complete with pseudo-code for the most important algorithms. All topics are copiously illustrated with color images and worked examples drawn from such application domains as biology, text processing, computer vision, and robotics. Rather than providing a cookbook of different heuristic methods, the book stresses a principled model-based approach, often using the language of graphical models to specify models in a concise and intuitive way. Almost all the models described have been implemented in a MATLAB software package—PMTK (probabilistic modeling toolkit)—that is freely available online. The book is suitable for upper-level undergraduates with an introductory-level college math background and beginning graduate students.
Advances In Probabilistic Deep Learning And Their Applications
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Author : Erik Alexander Daxberger
language : en
Publisher:
Release Date : 2024
Advances In Probabilistic Deep Learning And Their Applications written by Erik Alexander Daxberger and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024 with categories.
Probabilistic Machine Learning For Finance And Investing
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Author : Deepak K. Kanungo
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2023-08-14
Probabilistic Machine Learning For Finance And Investing written by Deepak K. Kanungo and has been published by "O'Reilly Media, Inc." this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-08-14 with Computers categories.
There are several reasons why probabilistic machine learning represents the next-generation ML framework and technology for finance and investing. This generative ensemble learns continually from small and noisy financial datasets while seamlessly enabling probabilistic inference, retrodiction, prediction, and counterfactual reasoning. Probabilistic ML also lets you systematically encode personal, empirical, and institutional knowledge into ML models. Whether they're based on academic theories or ML strategies, all financial models are subject to modeling errors that can be mitigated but not eliminated. Probabilistic ML systems treat uncertainties and errors of financial and investing systems as features, not bugs. And they quantify uncertainty generated from inexact inputs and outputs as probability distributions, not point estimates. This makes for realistic financial inferences and predictions that are useful for decision-making and risk management. Unlike conventional AI, these systems are capable of warning us when their inferences and predictions are no longer useful in the current market environment. By moving away from flawed statistical methodologies and a restrictive conventional view of probability as a limiting frequency, you’ll move toward an intuitive view of probability as logic within an axiomatic statistical framework that comprehensively and successfully quantifies uncertainty. This book shows you how.
Probabilistic Machine Learning
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Author : Kevin Murphy
language : en
Publisher:
Release Date : 2022
Probabilistic Machine Learning written by Kevin Murphy and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022 with categories.
Introduction To Deep Learning A Beginner S Edition
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Author : Harshitha Raghavan Devarajan
language : en
Publisher: INENCE PUBLICATIONS PVT LTD
Release Date : 2024-08-10
Introduction To Deep Learning A Beginner S Edition written by Harshitha Raghavan Devarajan and has been published by INENCE PUBLICATIONS PVT LTD this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-08-10 with Antiques & Collectibles categories.
"Introduction to Deep Learning: A Beginner’s Edition" is a comprehensive guide designed specifically for newcomers to the field of deep learning. This book provides an accessible introduction to the fundamental concepts, making it an ideal starting point for those who are curious about artificial intelligence and its rapidly expanding applications. The book begins with a clear explanation of what deep learning is and how it differs from traditional machine learning, covering the basics of neural networks and how they are used to recognize patterns and make decisions. One of the key strengths of this book is its practical, hands-on approach. Readers are guided through the process of building, training, and deploying neural networks using popular frameworks like TensorFlow and PyTorch. The step-by-step instructions, along with code snippets, allow even those with little to no programming experience to engage actively with the material. Visual aids, such as diagrams and flowcharts, are used throughout the book to simplify complex topics, making it easier for readers to grasp the inner workings of neural networks. The book also explores real-world applications of deep learning, highlighting its impact across various industries, including healthcare, autonomous vehicles, and natural language processing. By providing context and practical examples, the book demonstrates how deep learning is being used to solve complex problems and transform industries. In addition to the core content, the book includes a glossary of key terms, quizzes, and exercises to reinforce learning. "Introduction to Deep Learning: A Beginner’s Edition" is more than just a textbook; it is a complete learning experience designed to equip beginners with the knowledge and skills needed to embark on a successful journey into the world of deep learning.
Machine Learning Second Edition
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Author : Kevin P. Murphy
language : en
Publisher: MIT Press
Release Date : 2020
Machine Learning Second Edition written by Kevin P. Murphy and has been published by MIT Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020 with Computers categories.
The second and expanded edition of a comprehensive introduction to machine learning that uses probabilistic models and inference as a unifying approach. This textbook offers a comprehensive and self-contained introduction to the field of machine learning, including deep learning, viewed through the lens of probabilistic modeling and Bayesian decision theory. This second edition has been substantially expanded and revised, incorporating many recent developments in the field. It has new chapters on linear algebra, optimization, implicit generative models, reinforcement learning, and causality; and other chapters on such topics as variational inference and graphical models have been significantly updated. The software for the book (hosted on github) is now implemented in Python rather than MATLAB, and uses state-of-the-art libraries including as scikit-learn, Tensorflow 2, and JAX.