├── .idea ├── Deeplearning.ai-Solutions.iml ├── encodings.xml ├── misc.xml ├── modules.xml ├── preferred-vcs.xml └── vcs.xml ├── 02.Improving Deep Neural Networks- Hyperparameter tuning- Regularization and Optimization └── 1.2Bias Variance.srt ├── GTC_Yukon.md ├── README.md ├── srt └── 01.Neural-Networks-and-Deep-Learning │ ├── 01.Introduction-to-Deep-Learning │ ├── 01.Welcome.srt │ ├── 02.What is a neural network.srt │ ├── 03.Supervised Learning with Neural Networks.srt │ ├── 04.Why is Deep Learning taking off.srt │ ├── 05.About this Course.srt │ └── 06.Course Resources.srt │ ├── 02.Neural-Networks-Basics │ ├── 01.Binary Classification.srt │ ├── 02.Logistic Regression.srt │ ├── 03.Logistic Regression Cost Function.srt │ ├── 04.Gradient Descent.srt │ ├── 05.Derivatives.srt │ ├── 06.More Derivative Examples.srt │ ├── 07.Computation graph.srt │ ├── 08.Derivatives with a Computation Graph.srt │ ├── 09.Logistic Regression Gradient Descent.srt │ ├── 10.Gradient Descent on m Examples.srt │ ├── 11.Vectorization.srt │ ├── 12.More Examples of Vectorization.srt │ ├── 13.Vectorizing Logistic Regression.srt │ ├── 14.Vectorizing Logistic Regression's Gradient Output.srt │ ├── 15.Broadcasting in Python.srt │ ├── 16.A note on python_numpy vectors.srt │ ├── 17.Quick tour of Jupyter_iPython Notebooks.srt │ ├── 18.Explanation of logistic regression cost function (optional).srt │ └── 19.Heroes of Deep Learning (Optional) Pieter Abeel interview.srt │ └── 03.Shallow neural networks │ ├── 01.Neural Networks Overview.srt │ ├── 02.Neural Network Representation.srt │ ├── 03.Computing a Neural Network's Output.srt │ ├── 04.Vectorizing across multiple examples.srt │ ├── 06.Activation functions.srt │ ├── 09.Gradient descent for Neural Networks .srt │ ├── 10.Backpropagation intuition.srt │ ├── 11.Random Initialization_SPS.srt │ └── 11.Random-Initialization.srt ├── thanks.md └── tools └── ConvertToUTF8.py /.idea/Deeplearning.ai-Solutions.iml: 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