https://github.com/mancinimassimiliano/DeepLearningLab

Exercise for the deep learning course
https://github.com/mancinimassimiliano/DeepLearningLab

Last synced: 9 days ago
JSON representation

Exercise for the deep learning course

• Host: GitHub
• URL: https://github.com/mancinimassimiliano/DeepLearningLab
• Owner: mancinimassimiliano
• License: mit
• Created: 2019-03-26T10:45:06.000Z (over 5 years ago)
• Default Branch: master
• Last Pushed: 2020-05-24T10:34:06.000Z (over 4 years ago)
• Last Synced: 2024-08-02T14:08:33.507Z (3 months ago)
• Language: Jupyter Notebook
• Size: 14.1 MB
• Stars: 8
• Watchers: 1
• Forks: 4
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

Awesome Lists containing this project

README

        
# Deep Learning Lab

A series of Deep Learning exercises to practice with [PyTorch](https://pytorch.org/).

Professor: [Elisa Ricci](https://scholar.google.ca/citations?user=xf1T870AAAAJ&hl=en)

Lab Instructors:

  * [Massimiliano Mancini](https://mancinimassimiliano.github.io/) (Email: [email protected])

  * [Subhankar Roy](https://scholar.google.it/citations?user=YfzgrDYAAAAJ&hl=en) (Email: [email protected])

  * [Aliaksandr Siarohin](https://scholar.google.it/citations?user=uMl5-k4AAAAJ&hl=en) (Email: [email protected])

  * [Andrea Simonelli](https://scholar.google.it/citations?user=wK2I1ZsAAAAJ&hl=en) (Email: [email protected])

## Introduction

This is the official github webpage of the Deep Learning course for the academic year 2018/19 taught at University of Trento by Prof. Elisa Ricci. Hands-on experience will be provided for the concepts taught during the lectures. [Google Colab](https://colab.research.google.com) will be used to run the codes in GPU environment.

N.B. Lab related queries should be directed to the Lab instructors.

## Programming Exercises

  * [Lab1: Train a multi-layer perceptron (MLP) on MNIST](https://github.com/mancinimassimiliano/DeepLearningLab/tree/master/Lab1)

    * [Build your very first Neural Network](https://github.com/mancinimassimiliano/DeepLearningLab/blob/master/Lab1/myFirstNN.ipynb)

    * [Visualize training dynamics with Tensorboard](https://github.com/mancinimassimiliano/DeepLearningLab/blob/master/Lab1/myFirstNN_solution_with_vis.ipynb)

  * [Lab2: Train Convolutional Neural Networks (CNNs)](https://github.com/mancinimassimiliano/DeepLearningLab/blob/master/Lab2)

    * [MLP for classifying *translated* MNIST](https://github.com/mancinimassimiliano/DeepLearningLab/blob/master/Lab2/non_centered_mlp.ipynb)

    * [Train a LeNet-5 for MNIST classification](https://github.com/mancinimassimiliano/DeepLearningLab/blob/master/Lab2/convolutional_neural_networks.ipynb)

  * [Lab3: Transfer Learning](https://github.com/mancinimassimiliano/DeepLearningLab/blob/master/Lab3)

    * [Fine-tuning Alexnet](https://github.com/mancinimassimiliano/DeepLearningLab/blob/master/Lab3/finetune_alexnet.ipynb)

    * [Batch Normalization from scratch](https://github.com/mancinimassimiliano/DeepLearningLab/blob/master/Lab3/batch_normalization.ipynb)

    * [Unsupervised Domain Adaptation](https://github.com/mancinimassimiliano/DeepLearningLab/blob/master/Lab3/domain_adaptation.ipynb)

 * [Lab4: Recurrent Neural Network](https://github.com/mancinimassimiliano/DeepLearningLab/blob/master/Lab4)

 * [Lab5: Generative Adversarial Network](https://github.com/mancinimassimiliano/DeepLearningLab/blob/master/Lab5)

### Prerequisites

  * Python3