Developing AI Applications: An Introduction
- 5h 9m
- Metin Karatas
- Rheinwerk Publishing Inc.
- 2024
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!
- Learn to program your own AI applications—even if you’ve never coded before!
- Get started without code using the KNIME platform and then expand your skills by programming with basic Python
- Work with neural networks, transfer learning, anomaly detection, reinforcement learning, and more
In this book, you'll learn about:
1. AI Algorithms
Understand the theory and structure behind the most useful AI models and methods and how they can be used to solve problems. Learn about artificial neural networks, anomaly detections, cluster analysis, text and image classification, reinforcement learning, and other algorithms.
2. Tools and Platforms
Install and use AI platforms like KNIME and Anaconda. See how tools like ChatGPT and libraries like XGBoost can enhance your AI applications.
3. Practical Exercises
Get step-by-step instructions for creating AI applications, whether you’re implementing them with no code on the KNIME platform or programming them using basic Python.
About the Author
Metin Karatas is an electrical and information technology engineer. He was the first person to teach AI when it was established as a subject in Bavarian schools and he’s a member of the AI curriculum commission in Bavaria. Metin also teaches programming, electrical engineering, project management, and other subjects at a technical school for vocational training. He is enthusiastic about researching cutting-edge technologies and combining theoretical understanding with practical experience.
In this Book
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Introduction
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Installation
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Artificial Neural Networks
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Decision Trees
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Convolutional Layers and Images
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Transfer Learning
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Anomaly Detection
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Text Classification
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Cluster Analysis
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AutoKeras
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Visual Programming Using KNIME
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Reinforcement Learning
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Genetic Algorithms
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ChatGPT and GPT-4
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DALL-E and Successor Models
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Outlook
