000			03083nam a22001937a 4500
005			20251024174503.0
008			251024b |||||||| |||| 00| 0 eng d
020			_a9798868810169
082			_a006.31 _bTES
100			_aTestas, Abdelaziz _925002
245			_aBuilding Scalable deep learning pipelines on AWS: _bdevelop, train, and deploy deep learning models
260			_aNew York _bApress _c2024
300			_axx, 760 p.
365			_aEURO _b49.99
500			_aTable of contents: Front Matter Pages i-xx Download chapter PDF Overview of Scalable Deep Learning Pipelines on AWS Abdelaziz Testas Pages 1-56 Setting Up a Deep Learning Environment on AWS Abdelaziz Testas Pages 57-113 Data Preparation with PySpark for Deep Learning Abdelaziz Testas Pages 115-211 Deep Learning with PyTorch for Regression Abdelaziz Testas Pages 213-273 Deep Learning with TensorFlow for Regression Abdelaziz Testas Pages 275-319 Deep Learning with PyTorch for Classification Abdelaziz Testas Pages 321-429 Deep Learning with TensorFlow for Classification Abdelaziz Testas Pages 431-488 Scalable Deep Learning Pipelines with Apache Airflow Abdelaziz Testas Pages 489-584 Techniques for Improving Model Performance Abdelaziz Testas Pages 585-701 Deploying and Monitoring Deep Learning Models Abdelaziz Testas Pages 703-738 Back Matter Pages 739-760 [https://link.springer.com/book/10.1007/979-8-8688-1017-6]
520			_aThis book is your comprehensive guide to creating powerful, end-to-end deep learning workflows on Amazon Web Services (AWS). The book explores how to integrate essential big data tools and technologies—such as PySpark, PyTorch, TensorFlow, Airflow, EC2, and S3—to streamline the development, training, and deployment of deep learning models. Starting with the importance of scaling advanced machine learning models, this book leverages AWS's robust infrastructure and comprehensive suite of services. It guides you through the setup and configuration needed to maximize the potential of deep learning technologies. You will gain in-depth knowledge of building deep learning pipelines, including data preprocessing, feature engineering, model training, evaluation, and deployment. The book provides insights into setting up an AWS environment, configuring necessary tools, and using PySpark for distributed data processing. You will also delve into hands-on tutorials for PyTorch and TensorFlow, mastering their roles in building and training neural networks. Additionally, you will learn how Apache Airflow can orchestrate complex workflows and how Amazon S3 and EC2 enhance model deployment at scale. By the end of this book, you will be equipped to tackle real-world challenges and seize opportunities in the rapidly evolving field of deep learning with AWS. You will gain the insights and skills needed to drive innovation and maintain a competitive edge in today’s data-driven landscape. (https://link.springer.com/book/10.1007/979-8-8688-1017-6)
650			_aDeep learning models--AWS _925834
942			_cBK _2ddc
999			_c10411 _d10411