This 19-part course teaches deep learning applied to computer vision. You'll learn core deep learning concepts and how to apply them in computer vision applications such as image classification and image generation.
What is Computer Vision? Why Computer Vision?
Computer Vision comprises of a set of computational techniques to understand visual data such as images and videos. Computer vision techniques are used for image classification, motion tracking, image generation, colorizing black-and-white images, etc. Recent advanced applications of Computer Vision techniques have a wide variety of applications - form helping diagnose whether or not a patient has a tumor, to powering the search feature on photo managing applications like Google Photos, allowing us to perform image searches such as "my dog on the beach".
The primary objectives of this course are as follows:
- Understand what machine learning is, and learn the gradient descent algorithm.
- Understand what deep learning is, and how deep learning differs from and relates to machine learning.
- Understand what neural networks are and how they are trained using back-propagation. (and train your own neural network).
- Understand the concept of computational graphs, a core idea (often overlooked in DL courses) foundational to understanding and implementing all sorts of complex neural network architectures.
- Understand how Convolutional Nets work and how to solve computer vision tasks like image classification.
- Understand Generative Adversarial Networks, and how they can be used for generating images!
Prerequisites: Python, Linear Algebra, Statistics and NumPy and Calculus 1 (differentiation and chain rule).
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