Metin Karatas

Developing AI Applications (eBook, ePUB)

An Introduction

• Format: ePub

Produktbeschreibung

This book opens with a clear introduction to AI fundamentals, covering its history and key concepts while guiding readers through installing essential tools like KNIME and AutoKeras. It begins by building a strong foundation in artificial neural networks and decision trees, enabling readers to grasp core AI methods. The journey then advances to convolutional layers for image classification, transfer learning, and anomaly detection, offering practical, beginner-friendly examples.
As the reader progresses, the book explores text classification, cluster analysis, and automated AI model creation with AutoKeras. Visual programming with KNIME is introduced to simplify complex AI workflows. Further chapters cover reinforcement learning and genetic algorithms, expanding the reader's skill set and preparing them for more advanced challenges. Hands-on exercises throughout reinforce concepts and practical application.
In its final chapters, the guide dives into cutting-edge AI tools by demonstrating how to leverage ChatGPT and DALL-E APIs, including prompt engineering and API programming. It concludes with an outlook on the future of AI, equipping readers with the knowledge and confidence to build and deploy their own AI-powered applications from start to finish.

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Produktdetails

• Verlag: Packt Publishing
• Seitenzahl: 405
• Erscheinungstermin: 18. Juni 2025
• Englisch
• ISBN-13: 9781806022489
• Artikelnr.: 74526147

Inhaltsangabe

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