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Python Deep Learning Cookbook

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Name		Name	Last commit message	Last commit date
Latest commit History 35 Commits
data		data
1. Understanding the Perceptron.ipynb		1. Understanding the Perceptron.ipynb
10. Convolutional Neural Networks- filters and parameter sharing.ipynb		10. Convolutional Neural Networks- filters and parameter sharing.ipynb
11. Applying pooling layers.ipynb		11. Applying pooling layers.ipynb
12. Optimizing with batch normalization.ipynb		12. Optimizing with batch normalization.ipynb
13. Padding and strides.ipynb		13. Padding and strides.ipynb
14. Experimenting with different types of initialization.ipynb		14. Experimenting with different types of initialization.ipynb
15. Implementing a convolutional autencoder.ipynb		15. Implementing a convolutional autencoder.ipynb
16. Applying a 1D CNN to text.ipynb		16. Applying a 1D CNN to text.ipynb
17. Augmenting images with computer vision techniques.ipynb		17. Augmenting images with computer vision techniques.ipynb
18. Finding facial key points.ipynb		18. Finding facial key points.ipynb
2. Building a multi-layer neural network.ipynb		2. Building a multi-layer neural network.ipynb
20. Transferring styles to images.ipynb		20. Transferring styles to images.ipynb
21. Understanding GANs.ipynb		21. Understanding GANs.ipynb
22. Implementing a simple RNN.ipynb		22. Implementing a simple RNN.ipynb
23. Adding Long Short-Term Memory (LSTM).ipynb		23. Adding Long Short-Term Memory (LSTM).ipynb
24. Using gated recurrent units (GRUs).ipynb		24. Using gated recurrent units (GRUs).ipynb
25. Implementing bidirectional RNNs.ipynb		25. Implementing bidirectional RNNs.ipynb
26. Adding attention.ipynb		26. Adding attention.ipynb
3. Getting started with activation functions.ipynb		3. Getting started with activation functions.ipynb
4. Hidden layers and hidden units.ipynb		4. Hidden layers and hidden units.ipynb
5. Implementing an autoencoder.ipynb		5. Implementing an autoencoder.ipynb
6. Tuning the loss function.ipynb		6. Tuning the loss function.ipynb
7. Different Optimizers.ipynb		7. Different Optimizers.ipynb
8. Improving generalization with regularization.ipynb		8. Improving generalization with regularization.ipynb
9. Adding dropout to prevent overfitting.ipynb		9. Adding dropout to prevent overfitting.ipynb
README.md		README.md

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