AutoKeras Deep Learning Automation (eBook, ePUB)
The Complete Guide for Developers and Engineers
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"AutoKeras Deep Learning Automation" AutoKeras Deep Learning Automation offers a thorough exploration of the evolving landscape of automated machine learning (AutoML) in deep learning, with a special focus on the capabilities and inner workings of the AutoKeras framework. Beginning with the foundational motivations behind automating deep learning model design, the book guides readers through the historical advances in neural architecture search and situates AutoKeras within the broader AutoML ecosystem. It dives into the challenges inherent to deep learning automation-such as search space comp...
"AutoKeras Deep Learning Automation" AutoKeras Deep Learning Automation offers a thorough exploration of the evolving landscape of automated machine learning (AutoML) in deep learning, with a special focus on the capabilities and inner workings of the AutoKeras framework. Beginning with the foundational motivations behind automating deep learning model design, the book guides readers through the historical advances in neural architecture search and situates AutoKeras within the broader AutoML ecosystem. It dives into the challenges inherent to deep learning automation-such as search space complexity and model evaluation-while providing a critical comparison with other frameworks, ensuring readers understand the unique value that AutoKeras brings to contemporary AI. Moving beyond theory, this book meticulously details the technical architecture of AutoKeras, unraveling its layered system and integration with the larger TensorFlow and Keras environments. Readers are guided through comprehensive topics including automated data preprocessing, feature engineering, hyperparameter optimization, and the nuances of neural architecture search. The text also highlights advanced capabilities-such as custom model blocks, domain-specific adaptations, transfer learning, and interoperability with cloud and distributed infrastructures-empowering practitioners to efficiently automate complex workflows across a variety of domains, modalities, and deployment scenarios. In its final chapters, the book addresses the demands of production-grade deployment and scalability, detailing best practices for model export, continuous monitoring, retraining, and compliance. Real-world case studies, cutting-edge research trends, and critical topics like interpretability, fairness, and responsible AI are thoroughly discussed, ensuring that readers are equipped not only with the technical skills, but also with a forward-looking perspective on the ethical and practical dimensions of automated deep learning. "AutoKeras Deep Learning Automation" stands as an indispensable guide for engineers, researchers, and decision-makers seeking to harness the power of automation in modern AI development.
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