Automatic Differentiation in Python and PyTorch
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๐๐๐ To save 40% on this book use the Discount Code: twitosip40 ๐๐๐ Deep dive into understanding automatic differentiation used by PyTorch autograd for deep learning with the help of Carl Osipov, who has spent over 15 years working on big data processing and machine learning in multi-core, distributed systems, such as service-oriented architecture and cloud computing platforms.
"Serverless Machine Learning in Action: With PyTorch on AWS" is a guide to bringing your experimental machine learning code to production using serverless capabilities from major cloud providers. Youโll start with best practices for your datasets, learning to bring VACUUM data-quality principles to your projects, and ensure that your datasets can be reproducibly sampled. Next, youโll learn to implement machine learning models with PyTorch, discovering how to scale up your models in the cloud and how to use PyTorch Lightning for distributed ML training.
When youโre done, youโll have the tools to easily bridge the gap between ML models and a fully functioning production system.