Stanford Lecture Collection | Convolutional Neural Networks for Visual Recognition

Ok, maybe you were busy flossing the cat, or assembling a dog from a kit and didn’t have time to check out the lectures for CS231n from Stanford, Spring 2017. Looky here:

Why You Should Watch This Series

One of the really interesting things that has happened over the last 10 years is that top universities are sharing their classes online. In the technical/computer world, both MIT and Stanford are leading the charge to share knowledge with the only admission being a connection to the Internet and a viewing device. There is a wealth of knowledge available both at the graduate and under graduate level.

But you already know all that. While other people are watching inane YouTube videos, you are using that time to actually learn.

So you want to know what the next wave of Stanford entrepreneurs is going to be building on? You should watch this 16 lecture class. Here’s the blurb from the YouTube channel:

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.

If you’re new to computer vision combined with machine learning, here’s all the background and current theory in one place. You should remember that this is university level. So while none of this requires a PhD in math, it does require more work than watching the latest cute kitten video.

And yes, this is what everyone is so excited about in the NVIDIA Jetson world.

7 Comments

  1. While wading deeper into deep learning… (See what I did there?)

    Andrew Ng attempts to give us noobs a sense of whose shoulders we are standing on.
    https://www.youtube.com/watch?v=-eyhCTvrEtE

    Myself? I give a huge shoutout to the hardcore video gamers over the last decade or so that were willing to spend big bucks on the best graphics processors money could buy. Who knew back then they could be ‘turned around backwards’ and used for vision processing and neural networks rather than video rendering.

  2. The Stanford Lecture #2 mentions that Google is sponsoring cloud time for the course.

    If you sign up for the Google Cloud Platform, your account is initially credited with $300.

    https://support.google.com/cloud/answer/7006543?hl=en

    Free cloud compute time is good…

    The TensorFlow for Poets tutorial made a believer out of me when it ran on my RPi.
    https://codelabs.developers.google.com/codelabs/tensorflow-for-poets/#0

    Darknet totally rocks. Easy to set up and non-trivial use cases out of the box.
    https://pjreddie.com/darknet/

  3. I am new to computer vision and machine learning, so I can’t wait to check out these Stanford lectures. Majority of experience in computers comes from business data, so trying something so mathematically based will be interesting for sure. Thanks for the link!

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