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Keras Deep Learning Essentials PDF

135 Pages·1.9 MB·English
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by Richard Johnson| 135 pages| 1.9| English

About Keras Deep Learning Essentials

Keras Deep Learning Essentials is an authoritative guide that equips practitioners, researchers, and aspiring Deep Learning engineers with the essential knowledge and hands-on techniques for building, optimizing, and deploying state-of-the-art neural networks using the Keras framework. Beginning with the fundamental mathematical principles behind Deep Learning and a survey of modern neural architectures, the book offers clear explanations of Keras’s design philosophy, its seamless integration with TensorFlow, and the complete pipeline from initial prototyping to scalable production inference. With a strong emphasis on practical environment setup, the book ensures readers are well-prepared to harness advanced hardware acceleration and library dependencies for robust model development. Each chapter delves into a core aspect of the Keras workflow, from model construction patterns utilizing the Sequential and Functional APIs to sophisticated techniques such as subclassing, transfer learning, and custom layer engineering. Readers master the intricacies of efficient data pipelines, advanced feature engineering, and data augmentation strategies, supported by real-world guidance on handling class imbalance, online data validation, and complex input modalities. Model training and optimization at scale are addressed through modern loss and metric engineering, distributed and multi-GPU strategies, and advanced debugging and profiling to ensure performance and reliability for the most demanding applications.

Detailed Information

Author:Richard Johnson
Pages:135
Language:English
File Size:1.9
Format:PDF
Price:FREE
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