Simon J. D. Prince is Honorary Professor of Computer Science at the University of Bath and author of Computer Vision: Models, Learning and Inference. A research scientist specializing in artificial intelligence and deep learning, he has led teams of research scientists in academia and industry at Anthropics Technologies Ltd, Borealis AI, and elsewhere.
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Contents Preface xiii Acknowledgements xv 1 Introduction 1 2 Supervised learning 17 3 Shallow neural networks 25 4 Deep neural networks 41 5 Loss functions 56 6 Fitting models 77 7 Gradients and initialization 96 8 Measuring performance 118 9 Regularization 138 10 Convolutional networks 161 11 Residual networks 186 12 Transformers 207 13 Graph neural networks 240 14 Unsupervised learning 268 15 Generative Adversarial Networks 275 16 Normalizing flows 303 17 Variational autoencoders 326 18 Diffusion models 348 19 Reinforcement learning 373 20 Why does deep learning work? 401 21 Deep learning and ethics 420 A Notation 436 B Mathematics 439 C Probability 448 Bibliography 462 Index 513
