Neural networks are at the heart of AI-so ensure you're on the cutting edge with this guide! For true beginners, get a crash course in Python and the mathematical concepts you'll need to understand and create neural networks. Or jump right into programming your first neural network, from implementing the scikit-learn library to using the perceptron learning algorithm. Learn how to train your neural network, measure errors, make use of transfer learning, implementing the CRISP-DM model, and more. Whether you're interested in machine learning, gen AI, LLMs, deep learning, or all of the above, this is the AI book you need!
Highlights include:
1) Network creation
2) Network training
3) Supervised and unsupervised learning
4) Reinforcement learning
5) Algorithms
6) Multi-layer networks
7) Deep neural networks
8) Back propagation
9) Transformers
10) Python
11) Mathematical concepts
12) TensorFlow
Sprache
Verlagsort
Verlagsgruppe
Rheinwerk Publishing Inc.
Editions-Typ
Produkt-Hinweis
Maße
Höhe: 252 mm
Breite: 175 mm
Dicke: 18 mm
Gewicht
ISBN-13
978-1-4932-2696-2 (9781493226962)
Schweitzer Klassifikation
... Preface ... 15
1 ... Introduction ... 17
1.1 ... Why Neural Networks? ... 17
1.2 ... About This Book ... 18
1.3 ... The Contents in Brief ... 19
1.4 ... Is This Bee a Queen Bee? ... 22
1.5 ... An Artificial Neural Network for the Bee Colony ... 23
1.6 ... From Biology to the Artificial Neuron ... 28
1.7 ... Classification and the Rest ... 32
1.8 ... Summary ... 39
1.9 ... Further Reading ... 39
PART I ... Up and Running ... 41
2 ... Starter Kit for Developing Neural Networks with Python ... 43
2.1 ... The Technical Development Environment ... 43
2.2 ... Summary ... 63
3 ... A Simple Neural Network ... 65
3.1 ... Background ... 65
3.2 ... Bring on the Neural Network! ... 65
3.3 ... Neuron Zoom-In ... 68
3.4 ... Step Function ... 73
3.5 ... Perceptron ... 75
3.6 ... Points in Space: Vector Representation ... 76
3.7 ... Horizontal and Vertical: Column and Line Notation ... 82
3.8 ... The Weighted Sum ... 84
3.9 ... Step-by-Step: Step Functions ... 85
3.10 ... The Weighted Sum Reloaded ... 85
3.11 ... All Together ... 86
3.12 ... Task: Robot Protection ... 89
3.13 ... Summary ... 91
3.14 ... Further Reading ... 91
4 ... Learning in a Simple Network ... 93
4.1 ... Background: Plans Are Being Made ... 93
4.2 ... Learning in Python Code ... 94
4.3 ... Perceptron Learning ... 94
4.4 ... Separating Line for a Learning Step ... 98
4.5 ... Perceptron Learning Algorithm ... 99
4.6 ... The Separating Lines or Hyperplanes for the Example ... 103
4.7 ... scikit-learn Compatible Estimator ... 106
4.8 ... scikit-learn Perceptron Estimator ... 113
4.9 ... Adaline ... 115
4.10 ... Summary ... 125
4.11 ... Further Reading ... 126
5 ... Multilayer Neural Networks ... 127
5.1 ... A Real Problem ... 127
5.2 ... Solving XOR ... 129
5.3 ... Preparations for the Launch ... 134
5.4 ... The Plan for Implementation ... 135
5.5 ... The Setup ("class") ... 136
5.6 ... The Initialization ("__init__") ... 138
5.7 ... Something for In-Between ("print") ... 141
5.8 ... The Analysis ("predict") ... 141
5.9 ... The Usage ... 143
5.10 ... Summary ... 145
6 ... Learning in a Multilayer Network ... 147
6.1 ... How Do You Measure an Error? ... 147
6.2 ... Gradient Descent: An Example ... 149
6.3 ... A Network of Sigmoid Neurons ... 157
6.4 ... The Cool Algorithm with Forward Delta and Backpropagation ... 158
6.5 ... A "fit" Run ... 170
6.6 ... Summary ... 178
6.7 ... Further Reading ... 178
7 ... Examples of Deep Neural Networks ... 179
7.1 ... Convolutional Neural Networks ... 179
7.2 ... Transformer Neural Networks ... 194
7.3 ... The Optimization Method ... 204
7.4 ... Preventing Overfitting ... 205
7.5 ... Summary ... 207
7.6 ... Further Reading ... 207
8 ... Programming Deep Neural Networks Using TensorFlow 2 ... 209
8.1 ... Convolutional Networks for Handwriting Recognition ... 209
8.2 ... Transfer Learning with Convolutional Neural Networks ... 223
8.3 ... Transfer Learning with Transformer Neural Networks ... 231
8.4 ... Summary ... 236
8.5 ... Further Reading ... 236
PART II ... Deep Dive ... 239
9 ... From Brain to Network ... 241
9.1 ... Your Brain in Action ... 241
9.2 ... The Nervous System ... 242
9.3 ... The Brain ... 243
9.4 ... Neurons and Glial Cells ... 245
9.5 ... A Transfer in Detail ... 247
9.6 ... Representation of Cells and Networks ... 249
9.7 ... Summary ... 251
9.8 ... Further Reading ... 251
10 ... The Evolution of Artificial Neural Networks ... 253
10.1 ... The 1940s ... 254
10.2 ... The 1950s ... 255
10.3 ... The 1960s ... 257
10.4 ... The 1970s ... 257
10.5 ... The 1980s ... 258
10.6 ... The 1990s ... 270
10.7 ... The 2000s ... 271
10.8 ... The 2010s ... 272
10.9 ... Summary ... 274
10.10 ... Further Reading ... 274
11 ... The Machine Learning Process ... 277
11.1 ... The CRISP-DM Model ... 277
11.2 ... Ethical and Legal Aspects ... 281
11.3 ... Feature Engineering ... 290
11.4 ... Summary ... 317
11.5 ... Further Reading ... 318
12 ... Learning Methods ... 319
12.1 ... Learning Strategies ... 319
12.2 ... Tools ... 345
12.3 ... Summary ... 350
12.4 ... Further Reading ... 350
13 ... Areas of Application and Real-Life Examples ... 351
13.1 ... Warm-Up ... 351
13.2 ... Image Classification ... 354
13.3 ... Dreamed Images ... 373
13.4 ... Deployment with Pretrained Networks ... 382
13.5 ... Summary ... 386
13.6 ... Further Reading ... 386
... Appendices ... 387
A ... Python in Brief ... 389
B ... Mathematics in Brief ... 417
C ... TensorFlow 2 and Keras ... 435
... The Authors ... 445
... Index ... 447
