Programming Neural Networks with Python (Paperback)

Joachim Steinwendner

ISBN 10: 1493226967 / ISBN 13: 9781493226962

Published by Rheinwerk Publishing Inc., 2025

About this Item

Paperback. 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 creation2) Network training3) Supervised and unsupervised learning4) Reinforcement learning5) Algorithms6) Multi-layer networks7) Deep neural networks 8) Back propagation9) Transformers10) Python11) Mathematical concepts12) TensorFlow Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.

Seller Inventory # 9781493226962

Bibliographic Details

• Title: Programming Neural Networks with Python (Paperback)
• Publisher: Rheinwerk Publishing Inc.
• Publication Date: 2025
• Language: English
• ISBN 10: 1493226967
• ISBN 13: 9781493226962
• Binding: Paperback
• Condition: new

About this title

Synopsis

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.