Joachim Steinwendner, Roland Schwaiger | Programming Neural Networks with Python. Your Practical Guide to Building Smart AI Systems with Machine Learning and Deep Learning (Rheinwerk Computing) (2025) [PDF, EPUB]
Автор: Joachim Steinwendner, Roland Schwaiger
Издательство: Rheinwerk Computing
ISBN: 978-1493226962
Жанр: Introductory & Beginning Programming, Python Programming, Artificial Intelligence & Semantics
Язык: Английский
Формат: PDF, EPUB
Качество: Изначально электронное (ebook)
Иллюстрации: Цветные и черно-белые
Описание: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
Читать фрагмент 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
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