Chapter
The Power of Machine Learning Abstraction Layer
1:44:40 - 1:50:11 (05:31)
Machine learning serves as an outstanding abstraction layer that has revolutionized the working of the compiler people. The programming model and operations composed by machine learning nerds have raised levels of abstraction high enough to enable auto-parallelizing compilers.
Clips
The breakthrough of producing a programming model for operations that can compose high levels of abstraction has enabled auto parallelizing compilers to emerge, providing powerful tools to solve problems in machine learning.
1:44:40 - 1:46:22 (01:41)
Summary
The breakthrough of producing a programming model for operations that can compose high levels of abstraction has enabled auto parallelizing compilers to emerge, providing powerful tools to solve problems in machine learning. This organic development of the community around machine learning is providing a high-level method to enable compilers to exploit such complexities.
ChapterThe Power of Machine Learning Abstraction Layer
Episode#131 – Chris Lattner: The Future of Computing and Programming Languages
PodcastLex Fridman Podcast
The development of full-stack machine learning involves dealing with a range of issues from compilers to APIs and requires coordination among developers working on different parts of the system.
1:46:22 - 1:47:35 (01:12)
Summary
The development of full-stack machine learning involves dealing with a range of issues from compilers to APIs and requires coordination among developers working on different parts of the system.
ChapterThe Power of Machine Learning Abstraction Layer
Episode#131 – Chris Lattner: The Future of Computing and Programming Languages
PodcastLex Fridman Podcast
New developments in optimizers and training approaches, combined with algorithmic improvements, are pushing the field of neural networks forward and drastically reducing training times.
1:47:35 - 1:50:11 (02:36)
Summary
New developments in optimizers and training approaches, combined with algorithmic improvements, are pushing the field of neural networks forward and drastically reducing training times.