Deep Learning for Computer Vision

Overview

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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 and object detection. Recent developments in neural network approaches have greatly advanced the performance of these state-of-the-art visual recognition systems. This course is a deep dive into details of neural-network based deep learning methods for computer vision. During this course, students will learn to implement, train and debug their own neural networks and gain a detailed understanding of cutting-edge research in computer vision. We will cover learning algorithms, neural network architectures, and practical engineering tricks for training and fine-tuning networks for visual recognition tasks.

Syllabus

Lecture 1: Introduction to Deep Learning for Computer Vision.
Lecture 2: Image Classification.
Lecture 3: Linear Classifiers.
Lecture 4: Optimization.
Lecture 5: Neural Networks.
Lecture 6: Backpropagation.
Lecture 7: Convolutional Networks.
Lecture 8: CNN Architectures.
Lecture 9: Hardware and Software.
Lecture 10: Training Neural Networks I.
Lecture 11: Training Neural Networks II.
Lecture 12: Recurrent Networks.
Lecture 13: Attention.
Lecture 14: Visualizing and Understanding.
Lecture 15: Object Detection.
Lecture 16: Detection and Segmentation.
Lecture 17: 3D Vision.
Lecture 18: Videos.
Lecture 19: Generative Models I.
Lecture 20: Generative Models II.
Lecture 21: Reinforcement Learning.
Lecture 22: Conclusion.