Starting the Udacity Self-Driving Car Engineer Nanodegree

While moving forward in learning computer vision, machine learning, deep learning, and so may other facets that comprise “AI”, I have enrolled in Udacity’s Self-Driving Car Engineer Nanodegree. I want to see and learn how a wide range of technologies can be combined to solve an incredibly challenging like that of controlling a self-driving car – not to mention having an opportunity to learn about this transformative technology. Also, a huge selling-point came from being able to “test drive” the course for a couple of days.

Here are some of the topics presented under the first term “Computer Vision and Deep Learning”:

• Finding Lane Lines
  • Region masking on images
  • Canny edge detection
  • Hough transform
• Introduction to Neural Networks
  • Linear regression
  • Logistic regression
  • Neural networks
  • Perceptron
  • Gradient descent
  • Backpropogation
• Miniflow
  • Graphs
  • Forward propagation
  • Learning and loss
  • Linear transform
  • Sigmoid function
  • Cost
  • Stochastic gradient descent
• Introduction to Tensorflow
• Deep Neural Networks
• Convolutional Neural Networks

All of these lead up the the first significant project – classifying traffic signs using computer vision and neural networks.