Lecture 1: Introduction to Deep Learning for Computer Vision

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Published 2020-08-10

Lecture 1 gives a broad introduction to computer vision and machine learning. We give a brief history of the two fields, starting in the 1950s and leading up to the modern explosion of deep neural networks. We preview some of the topics we will cover in the rest of the course, and discuss the enormous potential of deep learning and computer vision to improve our lives. We also discuss the logistics and philosophy of this course.

Slides: myumi.ch/yKgM3
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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.

Course Website: myumi.ch/Bo9Ng

Instructor: Justin Johnson myumi.ch/QA8Pg

All Comments (21)

@mikeross4081 3 years ago

Easily one of the best current online courses for Deep Learning. Not only is this guy clearly an expert but he is just so enthusiastic about the subject. He doesn't just lecture but he actually teaches you. I love it when he says 'more concretely'. That always reminds me of Andrew Ng - another brilliant teacher of machine learning. Andrew's lectures are legendary! Anyway, well done Justin - keep up the good work!
@1jovi315 5 months ago

Can't believe he was teaching for the first time and he managed to make it so good and interesting. Really good job.
@mueez.mp4 3 years ago

This has to one of the best intros to CV ever! I'm in awe of the concise summary of the most influential papers in this field.
@glowish1993 5 months ago

Standing ovation for Michigan Online casually dropping one of the clearest, best explanations of deep learning online, all for free! We truly live in amazing times
@yassiraladdin5886 3 years ago

Thank you so much Justin. The best lecturer evveerr💯 From Sudan 🇸🇩✌️
@hileamlakyitayew9450 1 year ago

An actual master piece, thank you so much!!
@Timkok621 3 years ago

Was looking for it for a while.
@berresengul9677 3 years ago

Amazing lecture!
@souhaiblouda3821 2 years ago

Thank you, very much sure this is a really good lecture series!
@TronSAHeroXYZ 3 years ago

Good lecture, thanks Justin.
@raphaelkalandadze9691 1 year ago

First of all, thank you so much for this amazing course. I have learned a lot from your lectures. Can I ask when this course will be updated?
@joncarter2287 3 years ago

This is a really good lecture series!
@_tube7194 3 years ago

Thanks, it is nice lecture from Ethiopia
@paulks9771 2 years ago

Thanks from Lithuania!
@jijie133 3 years ago

Great!
@user-lw8yc5mk3h 8 months ago

awesome
@sanjivgautam9063 3 years ago

I remember Justin from Andrej Karpathy cs231 course. It was a terrific course. Would love to check this courses again as I can see Transformer and other stuffs added here in his playlist.
@user-pl5hj8zy1h 4 months ago

amazing
@kyungryunlee9626 2 years ago

Is there a special reason why we use pytorch instead of tensorflow(2.0) for education?? I mean for example the assignments. Is it Because its more intuitive and user friendly for beginners?
@joshuakartheek5554 1 month ago

In which lecture does Prof. Justin Johnson cover autoencoders?