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CourseComputer VisionLast updated 3 days ago

Learn Deep Learning for Computer Vision

This course consists of ~10 tutorials to learn deep learning applied to computer vision. You can think of this course as a "Free Online Nano Book". You'll learn core deep learning concepts and how to apply them in computer vision applications such as image classification and image generation. Everything is 100% free.

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".

An Introduction to Deep Learning and Neural Networks

We start by reviewing what core concepts in machine learning. Then we discuss how deep learning differs from machine learning, i.e. what deep means and why it is important. We'll learn specifically what neural networks look like and how they are trained using back-propagation. We'll also introduce the concept of computational graphs which is how neural networks are implemented in popular deep learning libraries such as TensorFlow, Torch, etc. We'll end the section with learning regularization techniques specific to deep learning.

A Visual Introduction (and Review) to Machine Learning
4 mins
What is deep learning? How does it relate to machine learning?
5 mins
A First Look at Neural Networks
9 mins
Computational Graphs and Backpropagation
11 mins
Regularization methods in Deep Learning
5 mins

Convolutional Neural Networks and Computer Vision

We'll start by introducing the image classification problem in computer vision and a small note on how images are stored in a computer. Thereafter, we'll describe the architecture of a Convolutional Neural Network (CNN) in substantial detail, and see why the convolutional layer is very well suited for computer vision applications.

Computer Vision tasks: Image classification, localization, etc
3 mins
How do computers see an image?
4 mins
Convolutional Neural Network
18 mins

Bonus: Generative Adversarial Networks and Image Generation

In this section (single tutorial), we'll learn about Generative Adversarial Networks (GANs). GANs are one of the few machine learning techniques which has given good performance for a variety of image generation related tasks. We'll introduce the image generation problem, describe the GAN setup and look at the results of applying GANs to image generation.

Generative Adversarial Networks
17 mins

9 items.

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A Visual Introduction (and Review) to Machine Learning

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