This course gives a theoretical and practical introduction to computer vision. The course will provide an introduction to convolutional neural networks, including the maths behind how 1D, 2D and 3D convolutional models work. We then run through the basics of TensorFlow 2, which is the framework of choice, and apply our knowledge to build and train simple multi-class image recognition models. We then go on to look at the challenges associated with image recognition, whilst gaining a basic knowledge of the OpenCV image manipulation library in the process. Since image recognition and classification on its own is only part of computer vision, our final activity will be to build and train a concatenated image and text model using the TensorFlow 2 API.
At the end of the course you will have an understanding of, and be able to build and train, your own image recognition models using your own data. This will provide you with the foundations to continue on your own journey in this fascinating field.
This course is very hands on and requires basic python, NumPy and pandas skills as well as a basic understanding of how artificial neural networks work. It is less maths intensive than the Neural Nets for Newbies course, since we will be using a deep learning framework as opposed to coding the algorithms by hand. This allows more time to build and train models. However a very basic understanding of linear algebra and differential calculus will go a long way