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Stanford University

Convolutional Neural Networks for Visual Recognition (Spring 2017)

Stanford University via YouTube

Overview

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. Instructors: Fei-Fei Li, Justin Johnson, Serena Yeung.

Syllabus

Lecture 1 | Introduction to Convolutional Neural Networks for Visual Recognition.
Lecture 2 | Image Classification.
Lecture 3 | Loss Functions and Optimization.
Lecture 4 | Introduction to Neural Networks.
Lecture 5 | Convolutional Neural Networks.
Lecture 6 | Training Neural Networks I.
Lecture 7 | Training Neural Networks II.
Lecture 8 | Deep Learning Software.
Lecture 9 | CNN Architectures.
Lecture 10 | Recurrent Neural Networks.
Lecture 11 | Detection and Segmentation.
Lecture 12 | Visualizing and Understanding.
Lecture 13 | Generative Models.
Lecture 14 | Deep Reinforcement Learning.
Lecture 15 | Efficient Methods and Hardware for Deep Learning.
Lecture 16 | Adversarial Examples and Adversarial Training.

Taught by

Stanford University School of Engineering

Reviews

4.6 rating, based on 5 Class Central reviews

Start your review of Convolutional Neural Networks for Visual Recognition (Spring 2017)

  • The course is very interesting and I would love to participate on more of this course. The class was so friendly I could ever imagine. Great lectures
  • Profile image for Ilya Rudyak
    Ilya Rudyak
    If you’re interested in Deep Learning chances are you’re familiar with brilliant cs231n CNN for Computer Vision. Personally I started with the 1st offering in 2015 which was taught by Andrej Karpathy (as you may see that was the only time when he taught it, then he moved to Tesla).

    If you are interested in more details including a comparison with more recent course by Justin Johnson in Michigan see my blog post (https://medium.com/@ilyarudyak/cs231n-vs-eecs-498-85536ae615).
  • Profile image for Emerging Tech
    Emerging Tech
    It was Awesome It was Awesome It was Awesome It was Awesome It was Awesome It was Awesome It was Awesome It was Awesome It was Awesome It was Awesome It was Awesome It was Awesome It was AwesomeIt was AwesomeIt was AwesomeIt was AwesomeIt was Awesome
  • Anonymous
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  • Anonymous
    This is very good course and very useful information about this course and like this course is artificial intelligence

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