PyTorch Tutorial: Deep Learning Framework Fundamentals - Fall 2022

Learn the fundamentals of PyTorch in this comprehensive tutorial lecture from the University of Utah's Data Science program. Explore essential deep learning framework concepts, starting with the distinction between dynamic and static frameworks and PyTorch's historical development. Master GPU utilization, work with numpy examples, and understand PyTorch statistics and abstractions. Practice hands-on with Python tensors, GPU operations, gradient recording, and model implementation. Dive into practical code examples covering device availability checks, autograde functionality, data loaders, batch processing, and essential Python imports. Gain experience with class implementations, linear operations, basic logic structures, and results analysis through real-world programming demonstrations.

Syllabus

Introduction
What is pytorch
Deep learning framework
Dynamic vs Static
Framework
pytorch history
use of GPU
numpy example
PyTorch statistics
PyTorch abstraction
Python tensor
Python to GPU
Gradient
Record
Model
Code examples
Check if device is available
Real code
Autograde
Data loader
Batch
pythonch
import library
record class
selflinear
basic logic
final result