Deep-Learning-Revision-Notes

Deep Learning Revision Notes

Name		Name	Last commit message	Last commit date
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0. MLflow model observation.ipynb		0. MLflow model observation.ipynb
1.Introduction to Deep Learning.ipynb		1.Introduction to Deep Learning.ipynb
10. Problem faces NN _ weight initialization.ipynb		10. Problem faces NN _ weight initialization.ipynb
11. Different Activation Functions.ipynb		11. Different Activation Functions.ipynb
12. Transfer Learning.ipynb		12. Transfer Learning.ipynb
13. Batch Normalization.ipynb		13. Batch Normalization.ipynb
14. Batch Normalization Practical.ipynb		14. Batch Normalization Practical.ipynb
15. Optimizers.ipynb		15. Optimizers.ipynb
16. Regularization.ipynb		16. Regularization.ipynb
17. Loss functions.ipynb		17. Loss functions.ipynb
18. Some General Guidlines.ipynb		18. Some General Guidlines.ipynb
2.Perceptron.ipynb		2.Perceptron.ipynb
3.Basic of ANN (Artificial Neural Network).ipynb		3.Basic of ANN (Artificial Neural Network).ipynb
4. Implementation of ANN using Keras.ipynb		4. Implementation of ANN using Keras.ipynb
5.Callback functions.ipynb		5.Callback functions.ipynb
6.Regression using ANN.ipynb		6.Regression using ANN.ipynb
7. Derivation of Necessary Mathematics.ipynb		7. Derivation of Necessary Mathematics.ipynb
8. Gradient Descent.ipynb		8. Gradient Descent.ipynb
9. Chain rule _ Backpropagation.ipynb		9. Chain rule _ Backpropagation.ipynb
README.md		README.md

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