This repository contains material related to Udacity's Deep Learning Nanodegree program. It consists of a bunch of tutorial notebooks for various deep learning topics. In most cases, the notebooks lead you through implementing models such as convolutional networks, recurrent networks, and GANs. There are other topics covered such as weight initialization and batch normalization.
- [Introduction to PyTorch]: Learn how to build neural networks in PyTorch and use pre-trained networks for state-of-the-art image classifiers.
- #1 ~ #3 : Basic of PyTorch (including tensor, define NET, feed-forward, Loss ftn, optimizer)
- #4~ #6 : Training with PyTorch (Fashion_MNIST, Inference, validation, save & load model)
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#7 : MLP for MNIST data
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[Convolutional Neural Networks]: Visualize the output of layers that make up a CNN. Learn how to define and train a CNN for classifying MNIST data, a handwritten digit database that is notorious in the fields of machine and deep learning. Also, define and train a CNN for classifying images in the CIFAR10 dataset.
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#1 ~ #2 : Cifar10 using CNN
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#3 : Transfer Learning VGG16