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Summer School 2021

Deep learning has been getting lots of attention from many different fields including business/finance, image handling and even science simulation. 2-Day Deep learning course in Summer School 2021 will be a good foundation course for who wants to actually program it. The course will proceed with presentation and live demonstration using Google Colab.

Day 1 will cover Multivariable linear regression and MNIST image classification using linear and Multi-Layer Perceptron (MLP) including some techniques to avoid overfitting. Day 2 will cover CIFAR10 image classification with Convolutional Neural Network and a simple example using Recurrent Neural Network.

* All coding will be done using Python 3 and PyTorch 1.8.

* Basic knowledge on Python and Object Oriented Programming is prerequisite for this course.

Contents


Day 1 - Morning (10:00 AM - 12:00 PM)

What we cover

  • Training linear model for Single/Multi-variable regression
  • Activation functions
  • Loss functions
  • Optimizer

Day 1 - Afternoon (1:00 PM - 4:00 PM)

What we cover

  • Multi-Layer Perceptron for linear regression
  • Overfitting
  • Hyper-parameters
  • Advanced techniques: Batch normalization, Xavier initialization, dropout

Day 2 - Morning (10:00 AM - 12:00 PM)

What we cover

  • Image classification for MNIST
  • Logistic model, cross entropy function
  • Convolutional Neural Network
  • Gradient Vanishing

Day 2 - Afternoon (1:00 PM - 4:00 PM)

What we cover

  • Recurrent Neural Network
  • Long Short Term Memory to predict a sine function

Acknowledgement

KAIST Idea Factory - [Deep learning alone]

Stanford [CS231n]

Pytorch document [1.5.0]

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