Course overview

In this course, you’ll learn the basics of neural networks, including graph representation, activation functions, multiple hidden layers, and image classification.

You’ll learn the different kinds of nonlinear activation functions, such as the ReLU function, hyperbolic tangent function, and others, to discover how they enable neural networks to capture nonlinearity. You’ll also learn how to add hidden layers — and how they can make neural networks more powerful.

At the end of the course, you’ll complete a project in which you will build a neural network to classify images of digits in the MNIST dataset — and tweak your neural networks for better handwriting recognition. This project is a chance for you to combine the skills you learned in this course and practice building neural networks using a typical deep learning workflow. This project can also serve as a portfolio project that you can show to future employers.

Key skills

    • Understanding the representation of neural networks
    • Demonstrating how adding hidden layers can improve model performance
    • Capturing nonlinearity in the data in neural networks

Course outline

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Projects in this course

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