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Deep Learning: A Practitioner's Approach,New
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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
⚠️ 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.
- Q: How many pages does the book have? A: Five hundred thirty pages. This provides an extensive overview of deep learning concepts and practical applications.
- Q: What binding type is used for this book? A: It has a paperback binding. This makes it lightweight and easy to handle for reading and reference.
- Q: What are the dimensions of this book? A: The book measures seven point zero one inches in length, one inch in width, and nine point one inches in height. These dimensions make it portable and suitable for various reading environments.
- Q: Is this book suitable for beginners? A: Yes, this book is suitable for beginners. It introduces deep learning concepts gradually, making it accessible for those new to the field.
- Q: How can I apply the techniques discussed in the book? A: You can apply the techniques by following the hands-on examples provided. These examples guide you through building deep learning networks using the Deeplearning4j library.
- Q: What is the reading level for this book? A: This book is intended for a general audience with some familiarity with programming. It is approachable for readers willing to learn about deep learning.
- Q: How should I store this book? A: Store it upright on a shelf to prevent bending. Keeping it in a dry, cool place will help maintain its condition.
- Q: Can I clean the book pages? A: No, avoid cleaning the pages with liquids. Dusting with a soft cloth is recommended to prevent damage.
- Q: Is this book safe for children? A: No, this book is not specifically targeted at children. It covers advanced concepts in deep learning that may not be suitable for younger readers.
- Q: What if my book arrives damaged? A: Contact customer support for assistance. They typically provide options for returns or exchanges if your book is damaged upon arrival.
- Q: Is there a warranty for this book? A: No, books generally do not come with a warranty. However, check with the retailer for their specific return policies.
- Q: What should I do if I have questions while reading? A: You can refer to online forums or communities focused on deep learning. Engaging with others can provide additional insights and support.
- Q: Is there a glossary in this book? A: Yes, the book includes a glossary of key terms. This helps readers understand the technical vocabulary used throughout the text.
- Q: Are there exercises included in the book? A: Yes, the book contains exercises. These are designed to reinforce the concepts taught and encourage hands-on practice.
- Q: What topics are covered in this book? A: The book covers deep learning fundamentals, major architectures, and practical applications using the Deeplearning4j library. It provides a comprehensive overview of the field.
- Q: Can I use this book for academic purposes? A: Yes, this book can be used for academic purposes. It serves as a valuable resource for courses related to machine learning and artificial intelligence.