Representing Neural Networks

Learn the representation and key terminology behind neural networks.


  • Learn how neural networks are represented visually.
  • Learn how to implement linear and logistic regression as neural networks.
  • Learn the differences between the nonlinear activation functions.

Mission Outline

1. Nonlinear Models
2. Introduction to Graphs
3. Computational Graphs
4. A Neural Network That Performs Linear Regression
5. Generating Regression Data
6. Fitting A Linear Regression Neural Network
7. Generating Classification Data
8. Implementing A Neural Network That Performs Classification
9. Next Steps
10. Takeaways

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.


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