Deep Learning Spring 2019 by Jen-Tzung Chien@ ECE NCTU Taiwan 授課教師：簡仁宗 (PS. This is my first time taking a Deep Learning class, the code might not be optimal, but I have tried my best to make it readable and clean)
HW score shown as below
-
HW1, Score 98/100
- Spec PDF
- Handcraft a DNN classifier from scratch, without any assistence from python module, i.e. no import Keras, torch nor tenserflow.
- An 0-1 classifier from the famous titanic dataset in Kaggle
- Analyze the correlation-coeffiecnt and find out the principal column element.
- Discuss the need of one-hot encoding.
- LaTeX report
- LaTeX report source code
-
HW2, Score 100/100
- Spec PDF
- CNN
- A traditional CNN classifier to classify the image of various types of animals (CUDA and NVIDIA GPU is required for accelerating the tasks.)
- Background knowledge with image processing might help this homework.
- PyTorch is allowed and used in this homework, but auto model build framework such as autoML is not allowed.
- RNN / LSTM
- Use RNN / LSTM for text analyzing and judge whether a paper with certain title will get accepted or not.
Word vectoris important, which is the fundamental of NLP- Compare the performance b/w RNN and LSTM as well as discussing the reason behind it.
- Try explain the reason of
Gradient VanishingorGradient Exploding
- LaTeX report
- LaTeX report source code
-
HW3, Score 95/100
- Spec PDF
- Variational Autoencoder
- Use VAE to compress images and resconstruct them.
- Background knowledge with probabilities and statistics might help this homework.
- CycleGAN
- Implement this paper
- Use CycleGAN to fake generate some anime/cartoon character from the other style.
- LaTeX report
- LaTeX report source code
- A
Style Transferto make picture of someone younger / older. - The original idea is taken from [here].(https://www.pytorchtutorial.com/pytorch-style-transfer/)
- Our team proposed 2 ways to implement, the CycleGAN from above and Neural Style Transfer from here.
- LaTeX report
- LaTeX report source code