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Learn Deep Learning for Computer Vision

Overview

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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Syllabus

Machine Learning and Gradient Descent

We'll start by describing what machine learning is, and introduce a simple learning algorithm: linear regression + gradient descent. Using this algorithm, we'll introduce the core concepts in machine learning: model parameters, cost function, optimization method, and overfitting and regularization.

What is Machine Learning? Why Machine Learning?
Free
11 mins
Linear Regression
Free
17 mins
Gradient Descent
14 mins
Hands-on Assignment: Implementing Linear Regression with Gradient Descent
12 mins
Generalization and Overfitting
9 mins
Strategies to Avoid Overfitting
11 mins
Quiz: Train vs Test Data, Overfitting, Cost functions
3 mins

An Introduction to Deep Learning and Neural Networks

We start by discussing what deep learning is, and 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.

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.

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.

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What is Machine Learning? Why Machine Learning?

Category: Computer Vision