https://github.com/krichelj/dl_bgu_2021

Deep Learning course, Computer Science M.Sc., Ben Gurion University of the Negev, Israel, 2021
https://github.com/krichelj/dl_bgu_2021

computer-science computer-vision computer-vision-algorithms convolutional-neural-networks deep-learning facial-recognition generative-adversarial-network neural-network nlp numpy recurrent-neural-networks rnn siamese-neural-network supervised-learning tensorboard tensorflow tensorflow-models text-generation

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Deep Learning course, Computer Science M.Sc., Ben Gurion University of the Negev, Israel, 2021

• Host: GitHub
• URL: https://github.com/krichelj/dl_bgu_2021
• Owner: krichelj
• Created: 2021-04-10T16:03:56.000Z (over 3 years ago)
• Default Branch: master
• Last Pushed: 2023-02-11T11:35:19.000Z (almost 2 years ago)
• Last Synced: 2024-12-21T06:21:03.692Z (10 days ago)
• Topics: computer-science, computer-vision, computer-vision-algorithms, convolutional-neural-networks, deep-learning, facial-recognition, generative-adversarial-network, neural-network, nlp, numpy, recurrent-neural-networks, rnn, siamese-neural-network, supervised-learning, tensorboard, tensorflow, tensorflow-models, text-generation
• Language: Python
• Homepage: https://shaykricheli.com/DL_BGU_2021/
• Size: 95.4 MB
• Stars: 0
• Watchers: 2
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# DL_BGU_2021

Deep Learning course, Computer Science M.Sc., Ben Gurion University of the Negev, Israel, 2021\

Nitzan Cohen and Joshua Shay Kricheli

### HW1 - Building A Neural Network from Scratch

[[report]](HW1/DL_HW_1_Report.pdf), [[code]](https://github.com/krichelj/DL_HWs/tree/master/HW1), [[data]](https://paperswithcode.com/dataset/mnist)

### HW2 - Facial Recognition using Siamese Networks for One Shot Learning

[[report]](HW2/DL_HW_2_Report.pdf), [[paper]](https://www.cs.cmu.edu/~rsalakhu/papers/oneshot1.pdf), [[code]](https://github.com/krichelj/DL_HWs/tree/master/HW2), [[data]](http://vis-www.cs.umass.edu/lfw/index.html)

### HW3 - Lyrics Generation Using RNNs

[[report]](HW3/DL_HW_3_Report.pdf), [[code]](https://github.com/krichelj/DL_HWs/tree/master/HW3), [[train data]](HW3/lyrics_train_set.csv), [[test data]](HW3/lyrics_test_set.csv)

### HW4 - Generative Adversarial Network

[[report]](HW4/DL_HW_4_Report.pdf), [[code]](https://github.com/krichelj/DL_HWs/tree/master/HW4), [[data set 1]](HW4/diabetes.arff), [[data set 2]](HW4/german_credit.arff)