Practical Deep Learning

A Python-Based Introduction

Häftad, Engelska, 2021

571 kr

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Beskrivning

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.

Produktinformation

Utgivningsdatum:2021-02-23
Mått:178 x 234 x 33 mm
Vikt:881 g
Format:Häftad
Språk:Engelska
Antal sidor:464
Förlag:No Starch Press,US
ISBN:9781718500747

Utforska kategorier

Programmeringsböcker inom Data och IT

Mer om författaren

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.

Recensioner i media

"Practical Deep Learning with Python is the perfect book for someone looking to break into deep learning. This book achieves an ideal balance between explaining prerequisite introductory material and exploring nuanced subtleties of the methods described. The reader will come away with a solid foundational understanding of the content as well as the practical knowledge required to apply the methods to real-world problems. Deep learning will continue to enable many breakthroughs in artificial intelligence applications and this book covers all that is needed to springboard into this exciting field."—Matt Wilder, longtime neural network practitioner and owner of Wilder AI, a deep learning consulting company"Kneusel’s book tackles machine learning (classification) fantastically, helping anyone with an interest to learn and turning that interest into a skillset for future machine learning projects."–GeekDude, GeekTechStuff

Innehållsförteckning

Foreword by Michael C. Mozer, PhDAcknowledgmentsIntroductionChapter 1: Getting StartedChapter 2: Using PythonChapter 3: Using NumPyChapter 4: Working With DataChapter 5: Building DatasetsChapter 6: Classical Machine LearningChapter 7: Experiments with Classical ModelsChapter 8: Introduction to Neural NetworksChapter 9: Training A Neural NetworkChapter 10: Experiments with Neural NetworksChapter 11: Evaluating ModelsChapter 12: Introduction to Convolutional Neural NetworksChapter 13: Experiments with Keras and MNISTChapter 14: Experiments with CIFAR-10Chapter 15: A Case Study: Classifying Audio SamplesChapter 16: Going FurtherIndex