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Deep Learning in Computer Vision (Coursera)

Deep learning added a huge boost to the already rapidly developing field of computer vision. With deep learning, a lot of new applications of computer vision techniques have been introduced and are now becoming parts of our everyday lives. These include face recognition and indexing, photo stylization or machine vision in self-driving cars. The goal of this course is to introduce students to computer vision, starting from basics and then turning to more modern deep learning models.

We will cover both image and video recognition, including image classification and annotation, object recognition and image search, various object detection techniques, motion estimation, object tracking in video, human action recognition, and finally image stylization, editing and new image generation. In course project, students will learn how to build face recognition and manipulation system to understand the internal mechanics of this technology, probably the most renown and oftenly demonstrated in movies and TV-shows example of computer vision and AI.

Who is this class for: The course is designed for people (1) who already know the basics of machine learning and deep learning (2) who have never studied image processing and computer vision and want to fill that gap (3) who want to learn how to solve computer vision problems with deep learning.

Course 6 of 7 in the Advanced Machine Learning Specialization

Syllabus

WEEK 1

Introduction to image processing and computer vision

Welcome to the “Deep Learning for Computer Vision“ course! In the first introductory week, you’ll learn about the purpose of computer vision, digital images, and operations that can be applied to them, like brightness and contrast correction, convolution and linear filtering. These simple image processing methods solve as building blocks for all the deep learning employed in the field of computer vision. Let’s get started!

Graded: Basic image processing

Graded: Image Alignment Notebook

WEEK 2

Convolutional features for visual recognition

Module two revolves around general principles underlying modern computer vision architectures based on deep convolutional neural networks. We’ll build and analyse convolutional architectures tailored for a number of conventional problems in vision: image categorisation, fine-grained recognition, content-based retrieval, and various aspect of face recognition. On the practical side, you’ll learn how to build your own key-points detector using a deep regression CNN.

Graded: Convolutional features for visual recognition

Graded: Facial keypoints notebook

WEEK 3

Object detection

In this week, we focus on the object detection task — one of the central problems in vision. We start with recalling the conventional sliding window + classifier approach culminating in Viola-Jones detector. Tracing the development of deep convolutional detectors up until recent days, we consider R-CNN and single shot detector models. Practice includes training a face detection model using a deep convolutional neural network.

Graded: Object Detection

Graded: Face detection notebook

WEEK 4

Object tracking and action recognition

The fourth module of our course focuses on video analysis and includes material on optical flow estimation, visual object tracking, and action recognition. Motion is a central topic in video analysis, opening many possibilities for end-to-end learning of action patterns and object signatures. You will learn to design computer vision architectures for video analysis including visual trackers and action recognition models.

Graded: Video Analysis

Graded: Face recognition notebook

WEEK 5

Image segmentation and synthesis

In the last module of this course, we shall consider problems where the goal is to predict entire image. These are semantic image segmentation and image synthesis problems. Modern CNNs tailored for segmentation employ multiple specialised layers to allow for efficient training and inference. Lastly, we will get to know Generative Adversarial Networks — a bright new idea in machine learning, allowing to generate arbitrary realistic images.

Graded: Image segmentation and synthesis

Graded: GAN notebook

ENROLL IN COURSE