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!

Your practical introduction to programming neural networks
Develop and train simple and multi-layer networks with Python
Learn about algorithms, activation functions, transformers, and more

The Basics
Learn about neural networks from the ground up! Understand how neural networks work and what their basic elements are, from algorithms and activation functions to transformers. Includes a primer on mathematics and Python for beginners!

Putting Theory into Practice
Develop different types of neural networks: simple ones, multi-layer ones, and even deep neural networks. Walk through diverse practical examples, from image classification to large language models (LLMs).

Letting the Machine's Learn
Train your newly created (or modified!) neural network. Get expert tips on skillfully using training data, selecting the right tools, increasing the hit rates of your models, and avoiding pitfalls.

Network creation
Network training
Supervised and unsupervised learning
Reinforcement learning
Algorithms
Multi-layer networks
Deep neural networks
Back propagation
Transformers
Python
Mathematical concepts
TensorFlow

About the Author

Dr. Joachim Steinwendner is a scientific project leader specializing in data science, machine learning, recommendation systems, and deep learning. He has been involved in the development of neural networks, guiding them from a cutting-edge research topic to their current everyday relevance, and has worked across various industries.

Dr. Roland Schwaiger is a software developer, freelance trainer, and consultant. He has a PhD in mathematics and he has spent many years working as a researcher in the development of artificial neural networks, applying them in the field of image recognition. In his work, he places great importance on bridging the gap between theory and practice. Whether as an author, consultant, or lecturer, he is passionate about sharing his enthusiasm with others.

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