Developing AI Applications
An Introduction
1st Edition
Published on 31. July 2024
Book
Paperback/Softback
402 pages
978-1-4932-2601-6 (ISBN)
Description
It's time to get practical about AI. Move past playing around with chatbots and plugging your data into others' applications-learn how to create your own! Walk through key AI methods like decision trees, convolutional layers, cluster analysis, and more. Get your hands dirty with simple no-code exercises and then apply that knowledge to more complex (but still beginner-friendly!) examples. With information on installing KNIME and using tools like AutoKeras, ChatGPT, and DALL-E, this guide will let you do more with AI!
Highlights include:
1) Python
2) KNIME
3) ChatGPT
4) DALL-E
5) Artificial neural networks (ANN)
6) Decision trees
7) Convolutional layers
8) Transfer learning
9) Anomaly detection
10) Text and image classification
11) Cluster analysis
12) Reinforcement learning
More details
Language
English
Place of publication
Bonn
Germany
Publishing group
Rheinwerk Publishing Inc.
Target group
Professional and scholarly
Edition type
New edition
Product notice
Klappenbroschur
Dimensions
Height: 248 mm
Width: 182 mm
Thickness: 22 mm
Weight
738 gr
ISBN-13
978-1-4932-2601-6 (9781493226016)
Schweitzer Classification
Person
Content
1 ... Introduction ... 15
1.1 ... What Does This Book Offer? ... 15
1.2 ... What Is Artificial Intelligence? ... 17
1.3 ... The History of AI: A Brief Overview ... 18
1.4 ... Development Tools Used in This Book ... 20
2 ... Installation ... 25
2.1 ... Anaconda Distribution ... 25
2.2 ... KNIME ... 30
3 ... Artificial Neural Networks ... 39
3.1 ... Classification ... 40
3.2 ... The Recipe ... 41
3.3 ... Building ANNs ... 45
3.4 ... Structure of an Artificial Neuron ... 47
3.5 ... Feed Forward ... 48
3.6 ... Back Propagation ... 51
3.7 ... Updating the Weights ... 53
3.8 ... ANN for Classification ... 55
3.9 ... Hyperparameters and Overfitting ... 63
3.10 ... Dealing with Nonnumerical Data ... 65
3.11 ... Dealing with Data Gaps ... 67
3.12 ... Correlation versus Causality ... 69
3.13 ... Standardization of the Data ... 76
3.14 ... Regression ... 78
3.15 ... Deployment ... 81
3.16 ... Exercises ... 85
4 ... Decision Trees ... 89
4.1 ... Simple Decision Trees ... 90
4.2 ... Boosting ... 100
4.3 ... XGBoost Regressor ... 109
4.4 ... Deployment ... 110
4.5 ... Decision Trees Using Orange ... 111
4.6 ... Exercises ... 115
5 ... Convolutional Layers and Images ... 117
5.1 ... Simple Image Classification ... 118
5.2 ... Hyperparameter Optimization Using Early Stopping and KerasTuner ... 123
5.3 ... Convolutional Neural Network ... 128
5.4 ... Image Classification Using CIFAR-10 ... 134
5.5 ... Using Pretrained Networks ... 137
5.6 ... Exercises ... 140
6 ... Transfer Learning ... 141
6.1 ... How It Works ... 143
6.2 ... Exercises ... 150
7 ... Anomaly Detection ... 151
7.1 ... Unbalanced Data ... 152
7.2 ... Resampling ... 156
7.3 ... Autoencoders ... 158
7.4 ... Exercises ... 164
8 ... Text Classification ... 165
8.1 ... Embedding Layer ... 165
8.2 ... GlobalAveragePooling1D Layer ... 168
8.3 ... Text Vectorization ... 170
8.4 ... Analysis of the Relationships ... 173
8.5 ... Classifying Large Amounts of Data ... 177
8.6 ... Exercises ... 180
9 ... Cluster Analysis ... 181
9.1 ... Graphical Analysis of the Data ... 182
9.2 ... The k-Means Clustering Algorithm ... 186
9.3 ... The Finished Program ... 189
9.4 ... Exercises ... 192
10 ... AutoKeras ... 193
10.1 ... Classification ... 194
10.2 ... Regression ... 195
10.3 ... Image Classification ... 196
10.4 ... Text Classification ... 199
10.5 ... Exercises ... 202
11 ... Visual Programming Using KNIME ... 203
11.1 ... Simple ANNs ... 204
11.2 ... XGBoost ... 223
11.3 ... Image Classification Using a Pretrained Model ... 227
11.4 ... Transfer Learning ... 232
11.5 ... Autoencoder ... 237
11.6 ... Text Classification ... 245
11.7 ... AutoML ... 249
11.8 ... Cluster Analysis ... 253
11.9 ... Time Series Analysis ... 257
11.10 ... Text Generation ... 271
11.11 ... Further Information on KNIME ... 277
11.12 ... Exercises ... 278
12 ... Reinforcement Learning ... 281
12.1 ... Q-Learning ... 282
12.2 ... Python Knowledge Required for the Game ... 287
12.3 ... Trainings ... 292
12.4 ... Test ... 294
12.5 ... Outlook ... 295
12.6 ... Exercises ... 296
13 ... Genetic Algorithms ... 297
13.1 ... The Algorithm ... 298
13.2 ... Example of a Sorted List ... 301
13.3 ... Example of Equation Systems ... 304
13.4 ... Real-Life Sample Application ... 306
13.5 ... Exercises ... 309
14 ... ChatGPT and GPT-4 ... 311
14.1 ... Prompt Engineering ... 313
14.2 ... The ChatGPT Programming Interface ... 328
14.3 ... Exercise 1: Math Support ... 344
15 ... DALL-E and Successor Models ... 345
15.1 ... DALL-E 2 ... 345
15.2 ... DALL-E 3 ... 350
15.3 ... Programming Interface ... 352
15.4 ... Exercise 1: DALL-E API with Moderation ... 357
16 ... Outlook ... 359
... Appendices ... 361
A ... Exercise Solutions ... 363
A.1 ... Chapter 3 ... 363
A.2 ... Chapter 4 ... 368
A.3 ... Chapter 6 ... 371
A.4 ... Chapter 7 ... 373
A.5 ... Chapter 8 ... 376
A.6 ... Chapter 9 ... 379
A.7 ... Chapter 10 ... 381
A.8 ... Chapter 11 ... 384
A.9 ... Chapter 12 ... 389
A.10 ... Chapter 13 ... 390
A.11 ... Chapter 14 ... 392
A.12 ... Chapter 15 ... 393
B ... References ... 395
C ... The Author ... 397
... Index ... 399
1.1 ... What Does This Book Offer? ... 15
1.2 ... What Is Artificial Intelligence? ... 17
1.3 ... The History of AI: A Brief Overview ... 18
1.4 ... Development Tools Used in This Book ... 20
2 ... Installation ... 25
2.1 ... Anaconda Distribution ... 25
2.2 ... KNIME ... 30
3 ... Artificial Neural Networks ... 39
3.1 ... Classification ... 40
3.2 ... The Recipe ... 41
3.3 ... Building ANNs ... 45
3.4 ... Structure of an Artificial Neuron ... 47
3.5 ... Feed Forward ... 48
3.6 ... Back Propagation ... 51
3.7 ... Updating the Weights ... 53
3.8 ... ANN for Classification ... 55
3.9 ... Hyperparameters and Overfitting ... 63
3.10 ... Dealing with Nonnumerical Data ... 65
3.11 ... Dealing with Data Gaps ... 67
3.12 ... Correlation versus Causality ... 69
3.13 ... Standardization of the Data ... 76
3.14 ... Regression ... 78
3.15 ... Deployment ... 81
3.16 ... Exercises ... 85
4 ... Decision Trees ... 89
4.1 ... Simple Decision Trees ... 90
4.2 ... Boosting ... 100
4.3 ... XGBoost Regressor ... 109
4.4 ... Deployment ... 110
4.5 ... Decision Trees Using Orange ... 111
4.6 ... Exercises ... 115
5 ... Convolutional Layers and Images ... 117
5.1 ... Simple Image Classification ... 118
5.2 ... Hyperparameter Optimization Using Early Stopping and KerasTuner ... 123
5.3 ... Convolutional Neural Network ... 128
5.4 ... Image Classification Using CIFAR-10 ... 134
5.5 ... Using Pretrained Networks ... 137
5.6 ... Exercises ... 140
6 ... Transfer Learning ... 141
6.1 ... How It Works ... 143
6.2 ... Exercises ... 150
7 ... Anomaly Detection ... 151
7.1 ... Unbalanced Data ... 152
7.2 ... Resampling ... 156
7.3 ... Autoencoders ... 158
7.4 ... Exercises ... 164
8 ... Text Classification ... 165
8.1 ... Embedding Layer ... 165
8.2 ... GlobalAveragePooling1D Layer ... 168
8.3 ... Text Vectorization ... 170
8.4 ... Analysis of the Relationships ... 173
8.5 ... Classifying Large Amounts of Data ... 177
8.6 ... Exercises ... 180
9 ... Cluster Analysis ... 181
9.1 ... Graphical Analysis of the Data ... 182
9.2 ... The k-Means Clustering Algorithm ... 186
9.3 ... The Finished Program ... 189
9.4 ... Exercises ... 192
10 ... AutoKeras ... 193
10.1 ... Classification ... 194
10.2 ... Regression ... 195
10.3 ... Image Classification ... 196
10.4 ... Text Classification ... 199
10.5 ... Exercises ... 202
11 ... Visual Programming Using KNIME ... 203
11.1 ... Simple ANNs ... 204
11.2 ... XGBoost ... 223
11.3 ... Image Classification Using a Pretrained Model ... 227
11.4 ... Transfer Learning ... 232
11.5 ... Autoencoder ... 237
11.6 ... Text Classification ... 245
11.7 ... AutoML ... 249
11.8 ... Cluster Analysis ... 253
11.9 ... Time Series Analysis ... 257
11.10 ... Text Generation ... 271
11.11 ... Further Information on KNIME ... 277
11.12 ... Exercises ... 278
12 ... Reinforcement Learning ... 281
12.1 ... Q-Learning ... 282
12.2 ... Python Knowledge Required for the Game ... 287
12.3 ... Trainings ... 292
12.4 ... Test ... 294
12.5 ... Outlook ... 295
12.6 ... Exercises ... 296
13 ... Genetic Algorithms ... 297
13.1 ... The Algorithm ... 298
13.2 ... Example of a Sorted List ... 301
13.3 ... Example of Equation Systems ... 304
13.4 ... Real-Life Sample Application ... 306
13.5 ... Exercises ... 309
14 ... ChatGPT and GPT-4 ... 311
14.1 ... Prompt Engineering ... 313
14.2 ... The ChatGPT Programming Interface ... 328
14.3 ... Exercise 1: Math Support ... 344
15 ... DALL-E and Successor Models ... 345
15.1 ... DALL-E 2 ... 345
15.2 ... DALL-E 3 ... 350
15.3 ... Programming Interface ... 352
15.4 ... Exercise 1: DALL-E API with Moderation ... 357
16 ... Outlook ... 359
... Appendices ... 361
A ... Exercise Solutions ... 363
A.1 ... Chapter 3 ... 363
A.2 ... Chapter 4 ... 368
A.3 ... Chapter 6 ... 371
A.4 ... Chapter 7 ... 373
A.5 ... Chapter 8 ... 376
A.6 ... Chapter 9 ... 379
A.7 ... Chapter 10 ... 381
A.8 ... Chapter 11 ... 384
A.9 ... Chapter 12 ... 389
A.10 ... Chapter 13 ... 390
A.11 ... Chapter 14 ... 392
A.12 ... Chapter 15 ... 393
B ... References ... 395
C ... The Author ... 397
... Index ... 399