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
Inhaltsverzeichnis
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
