Programming Neural Networks with Python

Joachim Steinwendner, Roland Schwaiger

• Publisher: Rheinwerk Computing
• Year: 2025
• Print ISBN: 9781493226962
• eBook ISBN: 9781493226979
• Edition: 1
• Topic: Computer Science
• Subtopic: Computer Science General
• Index: Computer Science

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

Table of contents

• Notes on Usage
• Table of Contents
•   Preface
• 1   Introduction
• Part I   Up and Running
• 2   Starter Kit for Developing Neural Networks with Python
• 3   A Simple Neural Network
• 4   Learning in a Simple Network
• 5   Multilayer Neural Networks
• 6   Learning in a Multilayer Network
• 7   Examples of Deep Neural Networks
• 8   Programming Deep Neural Networks Using TensorFlow 2
• Part II   Deep Dive
• 9   From Brain to Network
• 10   The Evolution of Artificial Neural Networks
• 11   The Machine Learning Process
• 12   Learning Methods
• 13   Areas of Application and Real-Life Examples
• A   Python in Brief
• B   Mathematics in Brief
• C   TensorFlow 2 and Keras
• The Authors
• Index
• Service Pages
• Legal Notes