Title
Deep Learning: A Practitioner's Approach,New
Delivery time: 8-12 business days (International)
Although interest in machine learning has reached a high point, lofty expectations often scuttle projects before they get very far. How can machine learningespecially deep neural networksmake a real difference in your organization? This handson guide not only provides the most practical information available on the subject, but also helps you get started building efficient deep learning networks.Authors Adam Gibson and Josh Patterson provide theory on deep learning before introducing their opensource Deeplearning4j (DL4J) library for developing productionclass workflows. Through realworld examples, youll learn methods and strategies for training deep network architectures and running deep learning workflows on Spark and Hadoop with DL4J. Dive into machine learning concepts in general, as well as deep learning in particular Understand how deep networks evolved from neural network fundamentals Explore the major deep network architectures, including Convolutional and Recurrent Learn how to map specific deep networks to the right problem Walk through the fundamentals of tuning general neural networks and specific deep network architectures Use vectorization techniques for different data types with DataVec, DL4Js workflow tool Learn how to use DL4J natively on Spark and Hadoop
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⚠️ WARNING (California Proposition 65):
This product may contain chemicals known to the State of California to cause cancer, birth defects, or other reproductive harm.
For more information, please visit www.P65Warnings.ca.gov.
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Shipping & Returns
Shipping
We ship your order within 2–3 business days for USA deliveries and 5–8 business days for international shipments. Once your package has been dispatched from our warehouse, you'll receive an email confirmation with a tracking number, allowing you to track the status of your delivery.
Returns
To facilitate a smooth return process, a Return Authorization (RA) Number is required for all returns. Returns without a valid RA number will be declined and may incur additional fees. You can request an RA number within 15 days of the original delivery date. For more details, please refer to our Return & Refund Policy page.
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Warranty
We provide a 2-year limited warranty, from the date of purchase for all our products.
If you believe you have received a defective product, or are experiencing any problems with your product, please contact us.
This warranty strictly does not cover damages that arose from negligence, misuse, wear and tear, or not in accordance with product instructions (dropping the product, etc.).
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Frequently Asked Questions
- Q: What is the focus of 'Deep Learning: A Practitioner's Approach'? A: This book focuses on practical applications of deep learning, specifically using the Deeplearning4j library to build efficient deep learning networks.
- Q: Who are the authors of this book? A: The book is authored by Adam Gibson and Josh Patterson, both of whom are experts in the field of machine learning and deep learning.
- Q: What topics are covered in this book? A: Key topics include deep learning theory, major deep network architectures, training methods, and the application of deep learning workflows on Spark and Hadoop.
- Q: Is this book suitable for beginners in deep learning? A: Yes, the book is designed to be accessible for those new to deep learning, providing foundational knowledge and hands-on guidance.
- Q: What is the publication date of the book? A: The book was published on September 12, 2017.
- Q: How many pages does this book have? A: The book contains a total of 530 pages.
- Q: What type of binding does this book have? A: This book is available in paperback binding.
- Q: Can this book help with specific deep learning problems? A: Yes, it provides strategies for mapping specific deep network architectures to relevant problems and tuning neural networks accordingly.
- Q: Does the book include real-world examples? A: Yes, it includes real-world examples to illustrate methods and strategies for training deep learning networks.
- Q: Is there a focus on any specific tools or libraries? A: The book specifically highlights the use of the Deeplearning4j library and its integration with Spark and Hadoop for deep learning workflows.