Practical Deep Learning: A Python-Based Introduction by Ron Kneusel: Used

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Book Title
Practical Deep Learning: A Python-Based Introduction
Publication Date
2021-02-23
ISBN
9781718500747
Kategorie

Über dieses Produkt

Product Identifiers

Publisher
No Starch Press, Incorporated
ISBN-10
1718500742
ISBN-13
9781718500747
eBay Product ID (ePID)
21038649490

Product Key Features

Number of Pages
464 Pages
Publication Name
Practical Deep Learning : a Python-Based Introduction
Language
English
Subject
Neural Networks, General, Programming Languages / Python
Publication Year
2021
Type
Textbook
Subject Area
Computers, Science
Author
Ronald T. Kneusel
Format
Trade Paperback

Dimensions

Item Height
1.1 in
Item Weight
31.7 Oz
Item Length
9.3 in
Item Width
7 in

Additional Product Features

Intended Audience
Trade
LCCN
2020-035097
Dewey Edition
23
Reviews
" 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
Illustrated
Yes
Dewey Decimal
006.31
Table Of Content
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
Synopsis
A book for people with no experience with machine learning and who are looking for an intuition-based, hands-on introduction using Python., 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., Practical Deep Learning teaches total beginners how to build the datasets and models needed to train neural networks for your own DL projects. If you've been curious about artificial intelligence and machine learning but didn't know where to start, this is the book you've been waiting for. Focusing on the subfield of machine learning known as deep learning , it explains core concepts and gives you the foundation you need to start building your own models. Rather than simply outlining recipes for using existing toolkits, Practical Deep Learning teaches you the why of deep learning and will inspire you to explore further. All you need is basic familiarity with computer programming and high school math--the book will cover the rest. After an introduction to Python, you'll move through key topics like how to build a good training dataset, work with the scikit-learn and Keras libraries, and evaluate your models' performance. You'll also learn: How to use classic machine learning models like k-Nearest Neighbors, Random Forests, and Support Vector Machines How neural networks work and how they're trained How to use convolutional neural networks How to develop a successful deep learning model from scratch You'll conduct experiments along the way, building to a final case study that incorporates everything you've learned. The perfect introduction to this dynamic, ever-expanding field, Practical Deep Learning will give you the skills and confidence to dive into your own machine learning projects.
LC Classification Number
Q325.5.K55 2021

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    The listing was for a hardcover version of this book; however, I received a paperback. The Seller replied quickly to my question about this issue and issued a full refund - and let me keep the book. So, a diligent Seller for sure - and well packaged and reasonable timing on shipping. Thank you for the refund, and as you suggested, I'll likely donate this volume and seek the hardcover.
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    Great transaction, exactly as described, packed well, and promptly shipped on August 6th. Unfortunately the U.S. Postal Service took 23 calendar days to deliver the book. It was shipped from Pennsylvania, to Atlanta, past Alabama to Texas, enjoyed several days in Texas, then to Minneapolis, Jacksonville, Florida, back to Atlanta, finally to Birmingham, and Huntsville. The seller was very responsive and I decided it was interesting to see if/how the book would arrive. Thanks, Joe
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