Convolutional Neural Networks for Visual Recognition

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Course overview

Lecture Details

Lecture 1 gives an introduction to the field of computer vision, discussing its history and key challenges. We emphasize that computer vision encompasses a wide variety of different tasks, and that despite the recent successes of deep learning we are still a long way from realizing the goal of human-level visual intelligence.

Keywords: Computer vision, Cambrian Explosion, Camera Obscura, Hubel and Wiesel, Block World, Normalized Cut, Face Detection, SIFT, Spatial Pyramid Matching, Histogram of Oriented Gradients, PASCAL Visual Object Challenge, ImageNet Challenge

Slides: http://cs231n.stanford.edu/slides/2017/cs231n_2017_lecture1.pdf

Course Details

Computer Vision has become ubiquitous in our society, with applications in search, image understanding, apps, mapping, medicine, drones, and self-driving cars. Core to many of these applications are visual recognition tasks such as image classification, localization and detection.

Recent developments in neural network (aka deep learning) approaches have greatly advanced the performance of these state-of-the-art visual recognition systems. This lecture collection is a deep dive into details of the deep learning architectures with a focus on learning end-to-end models for these tasks, particularly image classification.

From this lecture collection, students will learn to implement, train and debug their own neural networks and gain a detailed understanding of cutting-edge research in computer vision.

Curriculum for this course

16 Lessons 19:30:20 Hours

Lecture

16 Lessons 19:30:20 Hours

Introduction to Convolutional Neural Networks for Visual Recognition
Preview 00:57:57
Image Classification
00:59:32
Loss Functions and Optimization
01:14:40
Introduction to Neural Networks
01:13:59
Convolutional Neural Networks
01:08:56
Training Neural Networks I
01:20:20
Training Neural Networks II
01:15:29
Deep Learning Software
01:18:07
CNN Architectures
01:17:39
Recurrent Neural Networks
01:13:09
Detection and Segmentation
01:14:25
Visualizing and Understanding
01:15:48
Generative Models
01:17:41
Deep Reinforcement Learning
01:04:01
Efficient Methods and Hardware for Deep Learning
01:16:52
Adversarial Examples and Adversarial Training
01:21:45