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Practical Deep Learning: A Python-Based Introduction

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Original price was: $59.99.Current price is: $47.64.

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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 MachinesHow neural networks work and how they’re trainedHow to use convolutional neural networksHow 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.

From the Publisher

Practical Deep Learning, 2nd Edition on black background with No Starch Press logoPractical Deep Learning, 2nd Edition on black background with No Starch Press logo

Review of Practical Deep Learning on black backgroundReview of Practical Deep Learning on black background

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About the Author

Ronald T. Kneusel earned a PhD in machine learning from the University of Colorado, Boulder, and has over 20 years of machine learning experience in industry. Kneusel is also the author of numerous books, including Math for Programming (2025), The Art of Randomness (2024), How AI Works (2023), Strange Code (2022), and Math for Deep Learning (2021), all from No Starch Press.

No Starch Press logo. A black circle with a white iron and a star in the center.No Starch Press logo. A black circle with a white iron and a star in the center.

About the Publisher

No Starch Press has published the finest in geek entertainment since 1994, creating both timely and timeless titles like Python Crash Course, Python for Kids, How Linux Works, and Hacking: The Art of Exploitation. An independent, San Francisco-based publishing company, No Starch Press focuses on a curated list of well-crafted books that make a difference. They publish on many topics, including computer programming, cybersecurity, operating systems, and LEGO. The titles have personality, the authors are passionate experts, and all the content goes through extensive editorial and technical reviews. Long known for its fun, fearless approach to technology, No Starch Press has earned wide support from STEM enthusiasts worldwide.

Publisher ‏ : ‎ No Starch Press
Publication date ‏ : ‎ February 23, 2021
Language ‏ : ‎ English
Print length ‏ : ‎ 464 pages
ISBN-10 ‏ : ‎ 1718500742
ISBN-13 ‏ : ‎ 978-1718500747
Item Weight ‏ : ‎ 1.71 pounds
Dimensions ‏ : ‎ 7.13 x 0.96 x 9.25 inches
Best Sellers Rank: #784,836 in Books (See Top 100 in Books) #282 in Computer Neural Networks #436 in Computer Programming Languages #540 in Python Programming
Customer Reviews: 4.8 4.8 out of 5 stars (55) var dpAcrHasRegisteredArcLinkClickAction; P.when(‘A’, ‘ready’).execute(function(A) { if (dpAcrHasRegisteredArcLinkClickAction !== true) { dpAcrHasRegisteredArcLinkClickAction = true; A.declarative( ‘acrLink-click-metrics’, ‘click’, { “allowLinkDefault”: true }, function (event) { if (window.ue) { ue.count(“acrLinkClickCount”, (ue.count(“acrLinkClickCount”) || 0) + 1); } } ); } }); P.when(‘A’, ‘cf’).execute(function(A) { A.declarative(‘acrStarsLink-click-metrics’, ‘click’, { “allowLinkDefault” : true }, function(event){ if(window.ue) { ue.count(“acrStarsLinkWithPopoverClickCount”, (ue.count(“acrStarsLinkWithPopoverClickCount”) || 0) + 1); } }); });

5 reviews for Practical Deep Learning: A Python-Based Introduction

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  1. Amazon Customer

    would highly recommend
    excellent for learning the subject even for a newbie or pro!

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  2. Deep South

    Approachable without sacrificing technical depth
    All technical writers should take notes from Kneusel! He provides a highly approachable introduction to the subject of machine learning, which thoroughly covers all fundamentals without sacrificing technical depth. It provides all of the necessary context to truly understand the subject matter and presents it in a logical progression that helps the reader build a sense of mastery. Highly recommend for engineers and hobbyists alike!

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  3. Paul Nord

    Practical
    Most books on deep learning start simply enough but quickly turn into graduate-level math textbooks after a chapter or two. Ron created a true practical guidebook that will get you running sophisticated analysis without all of the theoretical background. Code examples produce graphical output and simple statistics that help the reader understand what they are doing and develop the intuition to compare different methods.

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  4. N. Vadulam

    Neither Practical nor useful.
    I bought this book hoping to learn Deep Learning.Unfortunately I found this book to be Neither Practical nor useful.I own this author’s both the books.Both of them are duds.The author merely starts the applications and leave them there without taking even one of them from beginning to end. This does not help.Any author who writes a book on Deep Learning should take at least one application (PLEASE leave out the IRIS data set!) from beginning to end to benefit the readers who pay a lot of money.What this author provides in his book is easily available online for free.

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  5. Acrhonoz

    Excelente material, sumamente pedagógico, bien estructurado, claro y conciso. La vara de entrada es saber programar en Python y conocer los fundamentos de NumPy.

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    Practical Deep Learning: A Python-Based Introduction
    Practical Deep Learning: A Python-Based Introduction

    Original price was: $59.99.Current price is: $47.64.

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