Practical Deep Learning
A Python-Based Introduction
by Ronald T. Kneusel
Publisher: No Starch Press
Book Details
| Print ISBN | 9781718500747 |
| eText ISBN | 9781718500754 |
| Publisher | No Starch Press |
| Publishing Year | 2021 |
| Language | English |
| Pages | 464 |
Practical Deep Learning: A Python-Based Introduction, published by No Starch Press, introduces core principles of machine learning and neural network design. The text uses Python alongside libraries such as scikit-learn and Keras to help readers build and evaluate functioning models.
The book examines foundational mathematics, practical dataset preparation, and classical machine learning algorithms. It also explores how artificial neural networks operate, how they are trained, and how to construct convolutional architectures.
Written for readers with basic programming experience and high school mathematics, the text pairs conceptual discussion with practical experimentation. Guided exercises run throughout the material, culminating in a comprehensive case study that integrates the covered techniques.
Table of Contents
Chapter 1: Getting Started
Chapter 2: Using Python
Chapter 3: Using NumPy
Chapter 4: Working With Data
Chapter 5: Building Datasets
Chapter 6: Classical Machine Learning
Chapter 7: Experiments with Classical Models
Chapter 8: Introduction to Neural Networks
Chapter 9: Training a Neural Network
Chapter 10: Experiments with Neural Networks
Chapter 11: Evaluating Models
Chapter 12: Introduction to Convolutional Neural Networks
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