PostgreSQL 9.6 High Performance - Second Edition: Optimize your database with configuration tuning, routine maintenance, monitoring tools, query optim Contributor(s): Ahmed, Ibrar (Author), Smith, Gregory (Author) |
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ISBN: 1784392979 ISBN-13: 9781784392970 Publisher: Packt Publishing OUR PRICE: $54.99 Product Type: Paperback - Other Formats Published: May 2017 * Not available - Not in print at this time * |
Additional Information |
BISAC Categories: - Computers | Databases - General |
Physical Information: 1.02" H x 7.5" W x 9.25" (1.90 lbs) 508 pages |
Descriptions, Reviews, Etc. |
Publisher Description: Solve different problems in modelling deep neural networks using Python, Tensorflow, and Keras with this practical guide Key Features
Book Description Deep Learning is revolutionizing a wide range of industries. For many applications, deep learning has proven to outperform humans by making faster and more accurate predictions. This book provides a top-down and bottom-up approach to demonstrate deep learning solutions to real-world problems in different areas. These applications include Computer Vision, Natural Language Processing, Time Series, and Robotics. The Python Deep Learning Cookbook presents technical solutions to the issues presented, along with a detailed explanation of the solutions. Furthermore, a discussion on corresponding pros and cons of implementing the proposed solution using one of the popular frameworks like TensorFlow, PyTorch, Keras and CNTK is provided. The book includes recipes that are related to the basic concepts of neural networks. All techniques s, as well as classical networks topologies. The main purpose of this book is to provide Python programmers a detailed list of recipes to apply deep learning to common and not-so-common scenarios. What You Will Learn
Who This Book Is For This book is intended for machine learning professionals who are looking to use deep learning algorithms to create real-world applications using Python. Thorough understanding of the machine learning concepts and Python libraries such as NumPy, SciPy and scikit-learn is expected. Additionally, basic knowledge in linear algebra and calculus is desired. |