https://github.com/vaibhavs10/10_days_of_deep_learning
10 days 10 different practical applications of Deep Learning (primarily NLP) using Tensorflow and Keras
https://github.com/vaibhavs10/10_days_of_deep_learning
classification gensim keras nlp python tensorflow tfidf-matrix
Last synced: 10 months ago
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10 days 10 different practical applications of Deep Learning (primarily NLP) using Tensorflow and Keras
- Host: GitHub
- URL: https://github.com/vaibhavs10/10_days_of_deep_learning
- Owner: Vaibhavs10
- Created: 2017-04-10T16:04:04.000Z (about 9 years ago)
- Default Branch: master
- Last Pushed: 2023-04-12T15:52:22.000Z (about 3 years ago)
- Last Synced: 2025-05-12T14:12:08.273Z (about 1 year ago)
- Topics: classification, gensim, keras, nlp, python, tensorflow, tfidf-matrix
- Language: Jupyter Notebook
- Size: 51.3 MB
- Stars: 31
- Watchers: 5
- Forks: 18
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
# 10 Days of Deep Learning
I took a challenge upon myself to fast pace my Deep Learning skills using the python data stack. I will take on a real life practical challenge everyday and try to solve it using Deep Learning (Starting from the basics)
## Day 1
> Text classification using Multi Layered Perceptron and comparing with NN's
#### TODO:
1. Use learning rate decay
2. Batch normalization
3. More number of epochs
## Day 2
> Text classification using TFIDF feature matrix and MLP
#### TODO:
1. Use LRD, Batch Normalisation
2. Use Dropout
3. Further process the TFIDF matrix using TruncatedSVD
## Day 3
> Text classification using word2vec and MLP
#### TODO:
1. Use LRD, Batch Normalisation, Dropout
2. Tweaking word2vec hyperparams
## Day 4
> Text classification using Convolutional Neural Networks
#### TODO:
1. Use LRD, Batch Normalisation, Dropout
2. Try a multi-layer Convolutional Network
## Day 5
> Text classification using Recurrent Neural Networks
#### TODO:
1. Use LRD, Batch Normalisation, Dropout
2. Use a combination of CNN and RNN