Programming Neural Networks with Python

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!
Highlights include:
1) Network creation
2) Network training
3) Supervised and unsupervised learning
4) Reinforcement learning
5) Algorithms
6) Multi-layer networks
7) Deep neural networks
8) Back propagation
9) Transformers
10) Python
11) Mathematical concepts
12) TensorFlow

Dr. Roland Schwaiger studied Computer Science at Bowling Green State University, Ohio, USA, and Applied Computer Science and Mathematics at the University of Salzburg, Austria, where he completed his doctorate in Mathematics. After several years of working as an assistant professor at the University of Salzburg, he joined SAP AG in 1996. There, he worked as a Human Resources software developer for three years, which gave him the opportunity to develop his skills in an exciting and inspirational working environment. In 1999, Roland became a freelance trainer, editor, consultant, and developer.