BDSI2019-ML

Notebook	Link
1 - Explore the Dataset
2 - Feature Extraction
3 - Getting X and y
4 - Clustering & Classification
5 - Regularization in Classification
6 - SGD
7 - Model Evaluation
8 - Hyperparameter & Model Selection
9 - Deep Learning
A1 - Decision Boundary
A2 - Backprop
A3 - More Neural Nets
B1 - RNN_Sine_Wave
B2 - Implement LSTMs
C1 - CNN_MNIST
C2 - CNN_Sentiment
C3 - Implement CNNs

Name		Name	Last commit message	Last commit date
Latest commit History 69 Commits
lib		lib
preprocessed		preprocessed
.gitignore		.gitignore
1 - Explore the Dataset.ipynb		1 - Explore the Dataset.ipynb
2 - Feature Extraction.ipynb		2 - Feature Extraction.ipynb
3 - Getting X and y.ipynb		3 - Getting X and y.ipynb
4 - Clustering & Classification.ipynb		4 - Clustering & Classification.ipynb
5 - Regularization in Classification.ipynb		5 - Regularization in Classification.ipynb
6 - SGD.ipynb		6 - SGD.ipynb
7 - Model Evaluation.ipynb		7 - Model Evaluation.ipynb
8 - Hyperparameter & Model Selection.ipynb		8 - Hyperparameter & Model Selection.ipynb
9 - Deep Learning.ipynb		9 - Deep Learning.ipynb
A1 - Decision Boundary.ipynb		A1 - Decision Boundary.ipynb
A2 - Backprop.ipynb		A2 - Backprop.ipynb
A3 - More Neural Nets.ipynb		A3 - More Neural Nets.ipynb
B1 - RNN_Sine_Wave.ipynb		B1 - RNN_Sine_Wave.ipynb
B2 - Implement LSTMs.ipynb		B2 - Implement LSTMs.ipynb
C1 - CNN_MNIST.ipynb		C1 - CNN_MNIST.ipynb
C2 - CNN_Sentiment.ipynb		C2 - CNN_Sentiment.ipynb
C3 - Implement CNNs.ipynb		C3 - Implement CNNs.ipynb
LICENSE		LICENSE
README.md		README.md
data.zip		data.zip
sentiment.zip		sentiment.zip

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