Practical Deep Learning
No Starch Press,US (Verlag)
978-1-7185-0074-7 (ISBN)
Deep Learning for Complete Beginners: A Python-Based Introduction is for complete beginners in machine learning. It introduces fundamental concepts such as classes and labels, building a dataset, and what a model is and does before presenting classic machine learning models, neural networks, and modern convolutional neural networks. Experiments in Python - working with leading open-source toolkits and standard datasets - give the reader hands-on experience with each model and help them build intuition about how to transfer the examples in the book to their own projects.
Ron Kneusel has been working in the machine learning industry since 2003 and has been programming in Python since 2004. He received a PhD in Computer Science from UC Boulder in 2016 and is the author of two previous books: Numbers and Computers and Random Numbers and Computers.
Foreword by Michael C. Mozer, PhD
Acknowledgments
Introduction
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
Chapter 13: Experiments with Keras and MNIST
Chapter 14: Experiments with CIFAR-10
Chapter 15: A Case Study: Classifying Audio Samples
Chapter 16: Going Further
Index
| Erscheinungsdatum | 12.03.2021 |
|---|---|
| Verlagsort | San Francisco |
| Sprache | englisch |
| Maße | 177 x 234 mm |
| Themenwelt | Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik |
| ISBN-10 | 1-7185-0074-2 / 1718500742 |
| ISBN-13 | 978-1-7185-0074-7 / 9781718500747 |
| Zustand | Neuware |
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