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Neural Networks and Deep Learning

These are the jupyter notebook asignments for the course Neural Networks and Deep Learning offered by Coursera and authored by DeeplearniNG.ai

1. Python Basics with numpy.

Welcome to your first programming exercise of the deep learning specialization. In this assignment you will:

  • Learn how to use numpy.

  • Implement some basic core deep learning functions such as the softmax, sigmoid, dsigmoid, etc...

  • Learn how to handle data by normalizing inputs and reshaping images.

  • Recognize the importance of vectorization.

  • Understand how python broadcasting works.

2. Logistic Regression with a Neural Network mindset.

Welcome to the first (required) programming exercise of the deep learning specialization. In this notebook you will build your first image recognition algorithm. You will build a cat classifier that recognizes cats with 70% accuracy!

As you keep learning new techniques you will increase it to 80+ % accuracy on cat vs. non-cat datasets. By completing this assignment you will:

  • Work with logistic regression in a way that builds intuition relevant to neural networks.

  • Learn how to minimize the cost function.

  • Understand how derivatives of the cost are used to update parameters.

3. Planar data classification with a hidden layer.

Welcome to the second programming exercise of the deep learning specialization. In this notebook you will generate red and blue points to form a flower. You will then fit a neural network to correctly classify the points. You will try different layers and see the results

By completing this assignment you will:

  • Develop an intuition of back-propagation and see it work on data.

  • Recognize that the more hidden layers you have the more complex structure you could capture.

  • Build all the helper functions to implement a full model with one hidden layer.

4. Building your Deep Neural Network: Step by Step.

Welcome to your third programming exercise of the deep learning specialization. You will implement all the building blocks of a neural network and use these building blocks in the next assignment to build a neural network of any architecture you want. By completing this assignment you will:

  • Develop an intuition of the over all structure of a neural network.

  • Write functions (e.g. forward propagation, backward propagation, logistic loss, etc...) that would help you decompose your code and ease the process of building a neural network.

  • Initialize/update parameters according to your desired structure.

5. Deep Neural Network - Application.

Congratulations! Welcome to the fourth programming exercise of the deep learning specialization. You will now use everything you have learned to build a deep neural network that classifies cat vs. non-cat images.