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Viser: Deep Learning Illustrated - A Visual, Interactive Guide to Artificial Intelligence
Deep Learning Illustrated Vital Source e-bog
Jon Krohn, Grant Beyleveld og Aglaé Bassens
(2019)
Deep Learning Illustrated Vital Source e-bog
Jon Krohn, Grant Beyleveld og Aglaé Bassens
(2019)
Deep Learning Illustrated Vital Source e-bog
Jon Krohn, Grant Beyleveld og Aglaé Bassens
(2019)
Deep Learning Illustrated
A Visual, Interactive Guide to Artificial Intelligence
Jon Krohn, Grant Beyleveld og Aglaé Bassens
(2019)
Sprog: Engelsk
om ca. 10 hverdage
Detaljer om varen
- 1. Udgave
- Vital Source 90 day rentals (dynamic pages)
- Udgiver: Pearson International (August 2019)
- Forfattere: Jon Krohn, Grant Beyleveld og Aglaé Bassens
- ISBN: 9780135121726R90
Bookshelf online: 90 dage fra købsdato.
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Detaljer om varen
- 1. Udgave
- Vital Source 365 day rentals (dynamic pages)
- Udgiver: Pearson International (August 2019)
- Forfattere: Jon Krohn, Grant Beyleveld og Aglaé Bassens
- ISBN: 9780135121726R365
Bookshelf online: 5 år fra købsdato.
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Detaljer om varen
- 1. Udgave
- Vital Source 180 day rentals (dynamic pages)
- Udgiver: Pearson International (August 2019)
- Forfattere: Jon Krohn, Grant Beyleveld og Aglaé Bassens
- ISBN: 9780135121726R180
Bookshelf online: 180 dage fra købsdato.
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Detaljer om varen
- 1. Udgave
- Paperback: 416 sider
- Udgiver: Pearson Education, Limited (Juli 2019)
- Forfattere: Jon Krohn, Grant Beyleveld og Aglaé Bassens
- ISBN: 9780135116692
Deep learning is one of today's hottest fields. This approach to machine learning is achieving breakthrough results in some of today's highest profile applications, in organizations ranging from Google to Tesla, Facebook to Apple. Thousands of technical professionals and students want to start leveraging its power, but previous books on deep learning have often been non-intuitive, inaccessible, and dry. In Deep Learning Illustrated, three world-class instructors and practitioners present a uniquely visual, intuitive, and accessible high-level introduction to the techniques and applications of deep learning. Packed with vibrant, full-color illustrations, it abstracts away much of the complexity of building deep learning models, making the field more fun to learn, and accessible to a far wider audience.
Part I's high-level overview explains what Deep Learning is, why it has become so ubiquitous, and how it relates to concepts and terminology such as Artificial Intelligence, Machine Learning, Artificial Neural Networks, and Reinforcement Learning. These opening chapters are replete with vivid illustrations, easy-to-grasp analogies, and character-focused narratives.
Building on this foundation, the authors then offer a practical reference and tutorial for applying a wide spectrum of proven deep learning techniques. Essential theory is covered with as little mathematics as possible, and illuminated with hands-on Python code. Theory is supported with practical "run-throughs" available in accompanying Jupyter notebooks, delivering a pragmatic understanding of all major deep learning approaches and their applications: machine vision, natural language processing, image generation, and videogaming.
To help readers accomplish more in less time, the authors feature several of today's most widely-used and innovative deep learning libraries, including TensorFlow and its high-level API, Keras; PyTorch, and the recently-released high-level Coach, a TensorFlow API that abstracts away the complexity typically associated with building Deep Reinforcement Learning algorithms.
Part I: Introducing Deep Learning 1
Chapter 1: Biological and Machine Vision 3 Biological Vision 3 Machine Vision 8 TensorFlow Playground 17 Quick, Draw! 19 Summary 19
Chapter 2: Human and Machine Language 21 Deep Learning for Natural Language Processing 21 Computational Representations of Language 25 Elements of Natural Human Language 33 Google Duplex 35 Summary 37
Chapter 3: Machine Art 39 A Boozy All-Nighter 39 Arithmetic on Fake Human Faces 41 Style Transfer: Converting Photos into Monet (and Vice Versa) 44 Make Your Own Sketches Photorealistic 45 Creating Photorealistic Images from Text 45 Image Processing Using Deep Learning 46 Summary 48
Chapter 4: Game-Playing Machines 49 Deep Learning, AI, and Other Beasts 49 Three Categories of Machine Learning Problems 53 Deep Reinforcement Learning 56 Video Games 57 Board Games 59 Manipulation of Objects 67 Popular Deep Reinforcement Learning Environments 68 Three Categories of AI 71 Summary 72
Part II: Essential Theory Illustrated 73
Chapter 5: The (Code) Cart Ahead of the (Theory) Horse 75 Prerequisites 75 Installation 76 A Shallow Network in Keras 76 Summary 84
Chapter 6: Artificial Neurons Detecting Hot Dogs 85 Biological Neuroanatomy 101 85 The Perceptron 86 Modern Neurons and Activation Functions 91 Choosing a Neuron 96 Summary 96 Key Concepts 97
Chapter 7: Artificial Neural Networks 99 The Input Layer 99 Dense Layers 99 A Hot Dog-Detecting Dense Network 101 The Softmax Layer of a Fast Food-Classifying Network 106 Revisiting Our Shallow Network 108 Summary 110 Key Concepts 110
Chapter 8: Training Deep Networks 111 Cost Functions 111 Optimization: Learning to Minimize Cost 115 Backpropagation 124 Tuning Hidden-Layer Count and Neuron Count 125 An Intermediate Net in Keras 127 Summary 129 Key Concepts 130
Chapter 9: Improving Deep Networks 131 Weight Initialization 131 Unstable Gradients 137 Model Generalization (Avoiding Overfitting) 140 Fancy Optimizers 145 A Deep Neural Network in Keras 147 Regression 149 TensorBoard 152 Summary 154 Key Concepts 155
Part III: Interactive Applications of Deep Learning 157
Chapter 10: Machine Vision 159 Convolutional Neural Networks 159 Pooling Layers 169 LeNet-5 in Keras 171 AlexNet and VGGNet in Keras 176 Residual Networks 179 Applications of Machine Vision 182 Summary 193 Key Concepts 193
Chapter 11: Natural Language Processing 195 Preprocessing Natural Language Data 195 Creating Word Embeddings with word2vec 206 The Area under the ROC Curve 217 Natural Language Classification with Familiar Networks 222 Networks Designed for Sequential Data 240 Non-sequential Architectures: The Keras Functional API 251 Summary 256 Key Concepts 257
Chapter 12: Generative Adversarial Networks 259 Essential GAN Theory 259 The Quick, Draw! Dataset 263 The Discriminator Network 266 The Generator Network 269 The Adversarial Network 272 GAN Training 275 Summary 281 Key Concepts 282
Chapter 13: Deep Reinforcement Learning 283 Essential Theory of Reinforcement Learning 283 Essential Theory of Deep Q-Learning Networks 290 Defining a DQN Agent 293 Interacting with an OpenAI Gym Environment 300 Hyperparameter Optimization with SLM Lab 303 Agents Beyond DQN 306 Summary 308 Key Concepts 309
Part IV: You and AI 311
Chapter 14: Moving Forward with Your Own Deep Learning Projects 313 Ideas for Deep Learning Projects 313 Resources for Further Projects 317 The Modeling Process, Including Hyperparameter Tuning 318 Deep Learning Libraries 321 Software
2.0 324 Approaching Artificial General Intelligence 326 Summary 328
Part V: Appendices 331 Appendix A: Formal Neural Network Notation 333 Appendix B: Backpropagation 335 Appendix C: PyTorch 339 PyTorch Features 339 PyTorch in Practice 341 Index 345