| Description |
1 online resource (656 p.) |
| Contents |
Intro -- Preface -- Goals of the book -- Responsible use of technology -- Structure of the book -- References -- Exercises -- Mathematical notation -- Acknowledgements -- Contents -- 1 The Deep Learning Revolution -- 1.1. The Impact of Deep Learning -- 1.1.1 Medical diagnosis -- 1.1.2 Protein structure -- 1.1.3 Image synthesis -- 1.1.4 Large language models -- 1.2. A Tutorial Example -- 1.2.1 Synthetic data -- 1.2.2 Linear models -- 1.2.3 Error function -- 1.2.4 Model complexity -- 1.2.5 Regularization -- 1.2.6 Model selection -- 1.3. A Brief History of Machine Learning |
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1.3.1 Single-layer networks -- 1.3.2 Backpropagation -- 1.3.3 Deep networks -- 2 Probabilities -- 2.1. The Rules of Probability -- 2.1.1 A medical screening example -- 2.1.2 The sum and product rules -- 2.1.3 Bayes' theorem -- 2.1.4 Medical screening revisited -- 2.1.5 Prior and posterior probabilities -- 2.1.6 Independent variables -- 2.2. Probability Densities -- 2.2.1 Example distributions -- 2.2.2 Expectations and covariances -- 2.3. The Gaussian Distribution -- 2.3.1 Mean and variance -- 2.3.2 Likelihood function -- 2.3.3 Bias of maximum likelihood -- 2.3.4 Linear regression |
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2.4. Transformation of Densities -- 2.4.1 Multivariate distributions -- 2.5. Information Theory -- 2.5.1 Entropy -- 2.5.2 Physics perspective -- 2.5.3 Differential entropy -- 2.5.4 Maximum entropy -- 2.5.5 Kullback-Leibler divergence -- 2.5.6 Conditional entropy -- 2.5.7 Mutual information -- 2.6. Bayesian Probabilities -- 2.6.1 Model parameters -- 2.6.2 Regularization -- 2.6.3 Bayesian machine learning -- Exercises -- 3 Standard Distributions -- 3.1. Discrete Variables -- 3.1.1 Bernoulli distribution -- 3.1.2 Binomial distribution -- 3.1.3 Multinomial distribution |
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3.2. The Multivariate Gaussian -- 3.2.1 Geometry of the Gaussian -- 3.2.2 Moments -- 3.2.3 Limitations -- 3.2.4 Conditional distribution -- 3.2.5 Marginal distribution -- 3.2.6 Bayes' theorem -- 3.2.7 Maximum likelihood -- 3.2.8 Sequential estimation -- 3.2.9 Mixtures of Gaussians -- 3.3. Periodic Variables -- 3.3.1 Von Mises distribution -- 3.4. The Exponential Family -- 3.4.1 Sufficient statistics -- 3.5. Nonparametric Methods -- 3.5.1 Histograms -- 3.5.2 Kernel densities -- 3.5.3 Nearest-neighbours -- Exercises -- 4 Single-layer Networks: Regression -- 4.1. Linear Regression |
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4.1.1 Basis functions -- 4.1.2 Likelihood function -- 4.1.3 Maximum likelihood -- 4.1.4 Geometry of least squares -- 4.1.5 Sequential learning -- 4.1.6 Regularized least squares -- 4.1.7 Multiple outputs -- 4.2. Decision theory -- 4.3. The Bias-Variance Trade-off -- Exercises -- 5 Single-layer Networks: Classification -- 5.1. Discriminant Functions -- 5.1.1 Two classes -- 5.1.2 Multiple classes -- 5.1.3 1-of-K coding -- 5.1.4 Least squares for classification -- 5.2. Decision Theory -- 5.2.1 Misclassification rate -- 5.2.2 Expected loss -- 5.2.3 The reject option -- 5.2.4 Inference and decision |
| Summary |
This book offers a comprehensive introduction to the central ideas that underpin deep learning. It is intended both for newcomers to machine learning and for those already experienced in the field. Covering key concepts relating to contemporary architectures and techniques, this essential book equips readers with a robust foundation for potential future specialization. The field of deep learning is undergoing rapid evolution, and therefore this book focusses on ideas that are likely to endure the test of time. The book is organized into numerous bite-sized chapters, each exploring a distinct topic, and the narrative follows a linear progression, with each chapter building upon content from its predecessors. This structure is well-suited to teaching a two-semester undergraduate or postgraduate machine learning course, while remaining equally relevant to those engaged in active research or in self-study. A full understanding of machine learning requires some mathematical background and so the book includes a self-contained introduction to probability theory. However, the focus of the book is on conveying a clear understanding of ideas, with emphasis on the real-world practical value of techniques rather than on abstract theory. Complex concepts are therefore presented from multiple complementary perspectives including textual descriptions, diagrams, mathematical formulae, and pseudo-code. Chris Bishop is a Technical Fellow at Microsoft and is the Director of Microsoft Research AI4Science. He is a Fellow of Darwin College Cambridge, a Fellow of the Royal Academy of Engineering, and a Fellow of the Royal Society. Hugh Bishop is an Applied Scientist at Wayve, a deep learning autonomous driving company in London, where he designs and trains deep neural networks. He completed his MPhil in Machine Learning and Machine Intelligence at Cambridge University. Chris Bishop wrote a terrific textbook on neural networks in 1995 and has a deep knowledge of the field and its core ideas. His many years of experience in explaining neural networks have made him extremely skillful at presenting complicated ideas in the simplest possible way and it is a delight to see these skills applied to the revolutionary new developments in the field. -- Geoffrey Hinton "With the recent explosion of deep learning and AI as a research topic, and the quickly growing importance of AI applications, a modern textbook on the topic was badly needed. The "New Bishop" masterfully fills the gap, covering algorithms for supervised and unsupervised learning, modern deep learning architecture families, as well as how to apply all of this to various application areas." Yann LeCun This excellent and very educational book will bring the reader up to date with the main concepts and advances in deep learning with a solid anchoring in probability. These concepts are powering current industrial AI systems and are likely to form the basis of further advances towards artificial general intelligence. -- Yoshua Bengio |
| Notes |
5.2.5 Classifier accuracy |
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Online resource; title from PDF title page (SpringerLink, viewed November 17, 2023) |
| Subject |
Deep learning (Machine learning)
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Deep learning (Machine learning)
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| Form |
Electronic book
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| Author |
Bishop, Hugh
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| ISBN |
9783031454684 |
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3031454685 |
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