Overview

TJ-100685 深度学习 | Deep learning (Elective Course)

Supervised by Professor, Yin Wang

GitHub Repo: hibetterheyj/tju_deep_learning

Course info

The course covers the following topics:

• Python basics
• Linear regression & Logistic regression
• NN basics
• Convolutional neural network
• Object detection
• Style transfer
• NLP basics
• Applicant: Medical image processing

Final Project

Enhanced 3D Zonal Segmentation of the Prostate on MRI via Enhanced Weight-Standardization and GroupNorm

基于的Weight-Standardization和GroupNorm的三维前列腺MRI区块分割

💥For more details, please refer to jupyter notebook for final project.

Assignments

Using nbviewer for better and faster rendering!

• (1) Linear & (2) Logistic (multiple binary classifiers method) regression

[notebook1], [notebook2]

• (1) Logistic (softmax method) regression & (2) Fully connected neural network

[notebook1], [notebook2]

• Convolutional neural network implemented via (1) PyTorch & (2) Fastai

[notebook1], [notebook2]

• Re-implemented one style transfer algorithm (MSG-Net) via PyTorch

[notebook]

All assignments are available in *.ipynb. Best wishes!