This is my repository for my works on Deep Learning Specialization course - I am going to make it more fun.

Note: Course label is Course1 for tracking all changes for this week

• Week 1: Intorductions
• Week 2: Logistic Regression as a Neural Network and Python Basics with Numpy(utils dir). This week label is Course1-Week2 for tracking the changes
  • loss function implementations L1, L2 methods added. (utils/loss.py)
  • image utils image2vector, display_image, flatten_X, and standardize_dataset methods added. (utils/image.py)
  • activation functions sigmoid , softmax, and sigmoid_derivative methods added.(utils/activation.py)
  • base utils, initialize_with_zeros and plot_cost_function added. (utils/base.py)
  • dataloader utils for loading different datasets added.(utils/dataloader.py)
  • normalization methods normalize_image and normalize_rows methods added.(utils/normalization.py)
  • LogisticRegression model with Neural Network mindset a directory with its own files added.
  • Dataset dataset/catvnotcat were used for this week added to dataset dir.
• Week 3: Planar data classification with one hidden layer directory
  • nn.py consist of nn model for classification of planner data, with an NN class
  • test_nn.py contains unit tests
  • util.py for visualization
  • nn.ipynb is a jupyter notebook file for demo of classification
• Week 4: Buidling DNN-Step by Step for CatvsNot Classification a dictionary with its own files added:
  • activations.py contains activations and their backward calculations for DNN
  • test_dnn.py contains unit tests
  • DNN.ipynb is a jupyter notebook file for demo of dnn classification
  • dnn.py is a deep neural network implementations. it contains DNN class as a base class and TwoLayerModel, LLayerModel classes as a child class of DNN.

Extra and useful links:

• https://www.cs.ryerson.ca/~aharley/neural-networks/
• https://cs231n.github.io/
• auto-reloading external module: http://stackoverflow.com/questions/1907993/autoreload-of-modules-in-ipython