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1
Intro
2
Fair Is Advancing Al in the Open
3
Translation at Facebook
4
Open Source Tools
5
Open Source Hardware
6
Twin Lakes
7
Tioga Pass
8
Bryce Canyon
9
PyTorch Ecosystem
10
Unframework
11
Machine Learning Execution Flow
12
Putting It Together
13
Research to Production at Facebook
14
Model development
15
Computation Graph Toolkits
16
Declarative Toolkits
17
Imperative Toolkits
18
Distributed Training Inside Each Iteration
19
Mobile Fragmentation
Description:
Save Big on Coursera Plus. 7,000+ courses at $160 off. Limited Time Only! Grab it Discover PyTorch 1.0, a groundbreaking open-source deep learning framework that bridges the gap between research and production in AI development. Explore how this innovative tool from Meta accelerates the transition from cutting-edge research to large-scale production, combining both research and production needs into a single, powerful framework. Learn about the PyTorch ecosystem, unframework approach, and machine learning execution flow. Gain insights into Facebook's AI innovation process, including model development, computation graph toolkits, and distributed training techniques. Understand how PyTorch 1.0 addresses mobile fragmentation and supports the advancement of AI in various domains, including open translation at Facebook. Delve into the open-source tools and hardware that contribute to the framework's effectiveness, such as Twin Lakes, Tioga Pass, and Bryce Canyon.

Introducing PyTorch 1.0 - A Research-Focused, Production-Ready Deep Learning Framework

Meta
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