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The Deep Learning Engineer's Handbook: From Fundamentals to Advanced Techniques with Scikit-Learn, Keras, and TensorFlow
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- FormatePub
- ISBN8231335404
- EAN9798231335404
- Date de parution16/05/2025
- Protection num.pas de protection
- Infos supplémentairesepub
- ÉditeurWalzone Press
Résumé
"The Deep Learning Engineer's Handbook: From Fundamentals to Advanced Techniques with Scikit-Learn, Keras, and TensorFlow" is a comprehensive guide designed for STEM professionals looking to master deep learning implementation. The book is structured to take readers from foundational concepts to advanced applications, covering essential neural network architectures, training methodologies, and deployment strategies.
This practical handbook features extensive code examples using popular frameworks like TensorFlow, Keras, and Scikit-Learn, enabling readers to build working models from scratch. The content progresses logically through machine learning fundamentals, convolutional neural networks, recurrent architectures, transformers, and generative models, culminating in production deployment techniques. What sets this handbook apart is its balance between theoretical understanding and practical implementation, with hands-on exercises that reinforce learning.
The book addresses both model development and operational concerns like monitoring, scaling, and maintaining deep learning systems in production environments. Perfect for engineers, data scientists, and researchers seeking to implement cutting-edge deep learning solutions, this handbook serves as both a learning resource and reference guide for building intelligent systems.
This practical handbook features extensive code examples using popular frameworks like TensorFlow, Keras, and Scikit-Learn, enabling readers to build working models from scratch. The content progresses logically through machine learning fundamentals, convolutional neural networks, recurrent architectures, transformers, and generative models, culminating in production deployment techniques. What sets this handbook apart is its balance between theoretical understanding and practical implementation, with hands-on exercises that reinforce learning.
The book addresses both model development and operational concerns like monitoring, scaling, and maintaining deep learning systems in production environments. Perfect for engineers, data scientists, and researchers seeking to implement cutting-edge deep learning solutions, this handbook serves as both a learning resource and reference guide for building intelligent systems.
"The Deep Learning Engineer's Handbook: From Fundamentals to Advanced Techniques with Scikit-Learn, Keras, and TensorFlow" is a comprehensive guide designed for STEM professionals looking to master deep learning implementation. The book is structured to take readers from foundational concepts to advanced applications, covering essential neural network architectures, training methodologies, and deployment strategies.
This practical handbook features extensive code examples using popular frameworks like TensorFlow, Keras, and Scikit-Learn, enabling readers to build working models from scratch. The content progresses logically through machine learning fundamentals, convolutional neural networks, recurrent architectures, transformers, and generative models, culminating in production deployment techniques. What sets this handbook apart is its balance between theoretical understanding and practical implementation, with hands-on exercises that reinforce learning.
The book addresses both model development and operational concerns like monitoring, scaling, and maintaining deep learning systems in production environments. Perfect for engineers, data scientists, and researchers seeking to implement cutting-edge deep learning solutions, this handbook serves as both a learning resource and reference guide for building intelligent systems.
This practical handbook features extensive code examples using popular frameworks like TensorFlow, Keras, and Scikit-Learn, enabling readers to build working models from scratch. The content progresses logically through machine learning fundamentals, convolutional neural networks, recurrent architectures, transformers, and generative models, culminating in production deployment techniques. What sets this handbook apart is its balance between theoretical understanding and practical implementation, with hands-on exercises that reinforce learning.
The book addresses both model development and operational concerns like monitoring, scaling, and maintaining deep learning systems in production environments. Perfect for engineers, data scientists, and researchers seeking to implement cutting-edge deep learning solutions, this handbook serves as both a learning resource and reference guide for building intelligent systems.