Deep Learning Patterns and Practices | AIBH New Arrival
| ISBN | 9781617298264 |
| Published Year | 2021 |
| Publisher | MANNING |
| Condition | New |
| Language | English |
| Edition | 1st |
| Binding | Paperback |
| Return Availability | Returnable upto 3days (*T&C applied) |
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Product Full Description
Deep Learning Patterns and Practices is a comprehensive guide that bridges the gap between deep learning research and practical application. Drawing from his experience at Google Cloud AI, Andrew Ferlitsch presents deep learning models as extendable design patterns, enabling developers to integrate them seamlessly into software projects.
The book delves into the internal workings of modern convolutional neural networks (CNNs), introducing procedural reuse design patterns for CNN architectures. It addresses the development of models suitable for mobile and IoT devices, strategies for assembling large-scale model deployments, and techniques for optimizing hyperparameter tuning. Additionally, it provides guidance on migrating models to production environments.
Through accessible diagrams, code samples, and real-world examples, readers will gain insights into deploying, testing, and maintaining deep learning applications. This resource is invaluable for machine learning engineers familiar with Python and deep learning, aiming to enhance their skills and confidence in building successful deep learning applications.
✅ Top 6 Key Features:
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Design Patterns for Deep Learning:
Introduces extendable design patterns for deep learning models, facilitating their integration into software projects. -
Comprehensive Coverage:
Covers topics from CNN architectures to model deployment strategies, providing a holistic view of deep learning practices. -
Practical Insights:
Offers real-world examples, code samples, and diagrams to illustrate complex concepts effectively. -
Production-Ready Guidance:
Provides strategies for migrating models from research to production environments, addressing common challenges. -
Focus on Mobile and IoT:
Discusses the development of models optimized for mobile and IoT devices, expanding the applicability of deep learning. -
Expert Authorship:
Authored by a seasoned expert with extensive experience in both research and practical implementation of AI technologies.
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