January 2025
Intermediate to advanced
698 pages
16h 48m
English
Content preview from Deep Learning and AI SuperheroBecome an O’Reilly member and get unlimited access to this title plus top books and audiobooks from O’Reilly and nearly 200 top publishers, thousands of courses curated by job role, 150+ live events each month,
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Chapter 4 Summary
In Chapter 4, we delved into PyTorch, one of the most widely used frameworks for deep learning. PyTorch's flexibility and dynamic computation graph have made it a favorite among researchers and practitioners alike. Throughout this chapter, we explored key PyTorch concepts, including how to build, train, and deploy models.
We began with an introduction to PyTorch’s dynamic computation graph, a key feature that sets it apart from other frameworks like TensorFlow (prior to version 2.x). Unlike static graphs, PyTorch's graph is created on-the-fly as operations are performed, allowing for greater flexibility in debugging and model design. This define-by-run approach makes it easier to handle models with dynamic architectures, such ...
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I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.