Tutorial  Introduction to PyTorch
Description
Professor Albert Bifet

Framework for Machine Learning.

Compare with other popular frameworks like TensorFlow.

Consists of two main components
 Numeric representation optimised for GPUs
 Deep Learning framework

Motivation
 Deep Learning using Python accelerates the path from research prototyping to production deployment
 Uses a dynamic computation graph approach
 Uses hardware accelerators as GPUs

Deep Learning
 Inspired by Biological neural networks, but not the same
 Construct improved features representing the input problem
 Combine features to improve network predictive capability

Neural Network

PyTorch audience

TensorFlow

Stochastic Gradient Descent
 Strategy for updating weights ('learning')
 Computed automatically within PyTorch

Skorch
 ScikitLearn wrapper for PyTorch

PyTorch example
 MNIST Dataset (National Institute of Standards and Technology)
 Classical dataset used within Deep Learning
 PyTorch data Loading
 PyTorch model
 Training & Evaluation
 Evaluating Training Loss
 Evaluating Testing Loss

TensorFlow
 Feature contrast with PyTorch
 Static topology
 Model building similar to PyTorch