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Practical Deep Learning:a Python-Based Introduction
[Paperback - 2021]
ronald t. kneusel
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Availability in 4-6 weeks on receipt of order
List Price: $59.99
Our Price: Rs.13895 Rs.11811
Standard Discount: 15%
You Save: Rs.2084
Category: Computer
Sub-category: Networking
Additional Category: Computer Science - Programming
Publisher: No Starch Press | ISBN: 9781718500747 | Pages: 464
Shipping Weight: .777 | Dimensions: 7.13 x .96 x 9.25 inches

Description

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 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.

    About the Author

    Ronald T. Kneusel is a data scientist who builds deep-learning (AI) systems, as well as extensive experience with medical imaging and the development of medical devices. He earned a PhD in machine learning from the University of Colorado, Boulder, has nearly 20 years of machine learning experience in industry, and is presently pursuing deep-learning projects with L3Harris Technologies, Inc. Kneusel is also the author of Random Numbers and Computers (Springer 2018), in addition to Math for Deep Learning, Practical Deep Learning, and Strange Code—all published by No Starch Press.

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