Lesson 7 of the Udacity Self-Driving Car Engineer Nanodegree Program is “Deep Neural Networks”
I am continuing on my quest to write a post detailing every one of the 67 projects that currently comprise our Self-Driving Car Engineer Nanodegree program curriculum, and today, we look at the “Deep Neural Networks” lesson!
Students actually start learning about deep neural networks prior to this lesson, but this is the lesson where students begin to implement deep neural networks in TensorFlow, Google’s deep learning framework.
In the previous lesson, “Introduction to TensorFlow,” students learned to use TensorFlow to build linear models, like linear or logistic regression. In the “Deep Neural Networks” lesson, students learn new techniques in TensorFlow, to build up these models into neural networks.
Some of the most important foundational blocks of neural networks are demonstrated in TensorFlow.
- Activation functions help neural networks represent non-linear models
- Backpropagation trains neural networks from real data quickly and efficiently
- Dropout removes neurons randomly during training to prevent overfitting the training data, which makes the model more accurate on new data
Students also learn some practical skills, like how to save and restore models in TensorFlow.
Future lessons take these basic skills and help students apply them to important problems for autonomous vehicles, like how to recognize traffic signs.