Wednesday, 19 Aug @ 9:30 PM

DESCRIPTION

In the AI world, deep learning (DL) is foundational for any application’s ability to receive and analyze new information and correctly deduce its meaning. Part 2 of this 3-part series addresses DL workloads and how the AI Kit helps developers make it so.

Continuing the momentum from August 5th, this webinar (which is Part 2 in a 3-part series) looks at the Intel® AI Analytics Toolkit from the perspective of deep learning (DL) workloads.

As in … performance benefits and features that can enhance DL training, inference, and workflows.

AGENDA / DISCUSSION TOPICS

Join software engineer Louis Tsai for this PART 2 session that delivers insights into the latest optimizations for Intel® Optimization for TensorFlow* and PyTorch which leverage the new acceleration instructions including Intel® DL Boost and BF16 support from 3rd Gen Intel® Xeon® Scalable processors.

Topics covered:

How to quantize a model from fp32/bf16 to int8 and analyze the performance speedup among different data types (fp32, bf16, and int8) in depth
Model Zoo for Intel® Architecture and low-precision tools included in the AI Kit
Efficiencies when building ML pipelines

PARTICIPANTS

Louie Tsai, Software Engineer, Intel Corporation

Webinar/Workshop organized by: Intel

iSeekh Disclaimer

We list events as per details provided by the organizer. We are NOT responsible or liable in case of any non-availability or deficiency of the service provided, including any paid event/services.