Introduction to Deep Learning in Pytorch

Approved: Yes
Title: Introduction to Deep Learning in Pytorch
Category: Learning
Skill Level:
Beginner (304), Intermediate (305)

Description:
This workshop series introduces the essential concepts in deep learning and
walks through the common steps in a deep learning workflow from data loading
and preprocessing to training and model evaluation. Throughout the sessions,
students participate in writing and executing simple deep learning programs
using Pytorch – a popular Python library for developing, training, and
deploying deep learning models.


Link to Resource:
- Landing Page (https://clemsonciti.github.io/rcde_workshops/pytorch/00-index.html)
- Pytorch Quickstart (https://clemsonciti.github.io/rcde_workshops/pytorch/00-quickstart.html)
- Pytorch Basics (https://clemsonciti.github.io/rcde_workshops/pytorch/01-pytorch_basics.html)
- Pytorch GPU Support (https://clemsonciti.github.io/rcde_workshops/pytorch/02-pytorch_gpu_support.html)
- Regression and Classification with Fully Connected Neural Networks (https://clemsonciti.github.io/rcde_workshops/pytorch/03-regression_and_classification.html)
- High Dimensional Data (https://clemsonciti.github.io/rcde_workshops/pytorch/04-high-dimensional-data.html)
- Datasets and data loading (https://clemsonciti.github.io/rcde_workshops/pytorch/05-datasets.html)
- Building the network (https://clemsonciti.github.io/rcde_workshops/pytorch/06-modules.html)
- Computer Vision and Convolutional Neural Networks (https://clemsonciti.github.io/rcde_workshops/pytorch/07-cnn_emnist.html)

Tags:
ai (271), deep-learning (303), image-processing (299), machine-learning (272), neural-networks (435), pytorch (471), gpu (80)

Domain:
ACCESS CSSN (780), Campus Champions (572), CAREERS (323), CCMNet (835), Great Plains (311), Kentucky (322), Northeast (308)

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