PyTorch: A flexible and intuitive framework for deep learning
James Bradbury offers an overview of PyTorch, a brand-new deep learning framework from developers at Facebook AI Research that's intended to be faster, easier, and more flexible than alternatives like TensorFlow. James makes the case for PyTorch, focusing on the library's advantages for natural language processing and reinforcement learning.
|Talk Title||PyTorch: A flexible and intuitive framework for deep learning|
|Speakers||James Bradbury (Salesforce Research)|
|Conference||Strata + Hadoop World|
|Conf Tag||Big Data Expo|
|Location||San Jose, California|
|Date||March 14-16, 2017|
The last few years have seen an explosion of interest in deep learning, but a data scientist new to the field faces an overwhelming array of open source software frameworks to choose from. James Bradbury makes the case for PyTorch, a brand-new deep learning framework from developers at Facebook AI Research, Twitter Cortex, and Salesforce Research that’s intended to be faster, easier, and more flexible than alternatives like TensorFlow. PyTorch is based on the efficient and well-tested Torch backend, but with a Python frontend built from the ground up for intuitive, rapid prototyping of new deep learning models for image, text, and time series data. James explains the define-by-run approach that makes PyTorch different and outlines examples from the fields of natural language processing and reinforcement learning that demonstrate its power and simplicity. Code will be made available for all examples used in the talk.