Computer Vision

This lesson introduces a way to represent motion mathematically, outlines what you'll learn in this section, and introduces optical flow.

Learn to implement a Bayesian filter to locate a robot in space and represent uncertainty in robot motion.

Write sense and move functions (and debug) a 2D histogram filter!

Learn the intuition behind the Kalman Filter, a vehicle tracking algorithm, and implement a one-dimensional tracker of your own.

Learn about representing the state of a car in a vector that can be modified using linear algebra.

Linear Algebra is a rich branch of math and a useful tool. In this lesson you'll learn about the matrix operations that underly multidimensional Kalman Filters.

Learn how to implement SLAM: simultaneously localize an autonomous vehicle and create a map of landmarks in an environment.

Review the basics of calculus and see how to derive the x and y components of a self-driving car's motion from sensor measurements and other data.

Implement SLAM, a robust method for tracking an object over time and mapping out its surrounding environment, using elements of probability, motion models, and linear algebra.

Try out a few really cool applications of computer vision and deep learning, such as style transfer, using pre-trained models that others have generously provided on Github.

Short introduction to neural networks: how they train by doing a feedforward pass then performing backpropagation.

Now that you know what neural networks are, in this lesson you will learn several techniques to improve their training.

Learn how to use PyTorch for building deep learning models

Sebastian Thrun teaches us about his groundbreaking work detecting skin cancer with convolutional neural networks.

In this lesson, Andrew Trask, the author of Grokking Deep Learning, will walk you through using neural networks for sentiment analysis.

Get a high-level overview of how fully-convolutional neural networks work, and see how they can be used to classify every pixel in an image.

The differences between C++ and Python and how to write C++ code.

To program matrix algebra operations and translate your Python code, you will need to use C++ Vectors. These vectors are similar to Python lists, but the syntax can be somewhat tricky.

Learn how to write C++ code on your own computer and compile it into a executable program without running into too many compilation errors.

Learn the syntax of C++ object oriented programming as well as some of the additional OOP features provided by the language.

In this lesson, we'll compare the execution times of C++ and Python programs.

Optimizing C++ involves understanding how a computer actually runs your programs. You'll learn how C++ uses the CPU and RAM to execute your code and get a sense for what can slow things down.

Now you understand how C++ programs execute. It's time to learn specific optimization techniques and put them into practice. This lesson will prepare you for the lesson's code optimization project.

Get ready to optimize some C++ code. You are provided with a working 2-dimensional histogram filter; your job is to get the histogram filter code to run faster!