Deep Learning Fundamentals

Learn the basics of deep neural networks.

This course will teach you graph theory basics, nonlinear activation functions, multiple hidden layers, and image classification.By the end of this course, you'll be able to:

  • Understand how neural networks are represented.
  • Understand how adding hidden layers can provide improved model performance.
  • Understand how neural networks capture nonlinearity in the data.

Course Info:

Deep Learning Fundamentals


The average completion time for this course is 10-hours.

This course requires a premium subscription. This course includes one free mission, two paid missions, and one guided project. It is the 23rd course in the Data Scientist in Python path.


Learn the Fundamentals of Deep Learning

Representing Neural Networks

Learn the representation and key terminology behind neural networks

Nonlinear Activation Functions

Learn about the different activation functions and how they enable neural networks to capture nonlinearity.

Hidden Layers

Learn about adding hidden layers to a neural network.

Building A Handwritten Digits Classifier

Learn the basics of image classification to build a handwritten classifier.