Explore a colloquium presentation on fast and accurate deep neural networks training delivered by Yang You from UC Berkeley. Dive into the increasing popularity of supercomputers in leading AI companies and their potential for accelerating deep learning computations. Learn about the challenges of parallelizing deep learning and discover innovative optimizers like LARS and LAMB, which enable better scaling and higher accuracy in real-world applications. Gain insights into record-breaking Imagenet training speeds, reduced BERT training times, and state-of-the-art results on various benchmarks. Understand how these approaches are being utilized by major tech companies and their impact on distributed systems and supercomputers in the field of machine learning.
Overview
Syllabus
Allen School Colloquium: Yang You (UC Berkeley)
Taught by
Paul G. Allen School
Related Courses
-
Do ImageNet Classifiers Generalize to ImageNet? - Analyzing ML Progress and Challenges
-
Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization
5.0 -
Deep Learning A-Z 2024: Neural Networks, AI & ChatGPT Prize
-
Domain Specific Architectures for Deep Neural Networks: Three Generations of Tensor Processing Units
-
A Decade of Machine Learning Accelerators: Lessons Learned and Carbon Footprint
-
Benchmarks and How-Tos for Convolutional Neural Networks on HorovodRunner-Enabled Apache Spark Clusters
