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Stanford University

Convolutional Neural Networks for Visual Recognition

Stanford University via YouTube

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, 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

4.4 rating, based on 5 Class Central reviews

Start your review of Convolutional Neural Networks for Visual Recognition

  • 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
  • Yadati Rakshith
    The content in this course is good. But there are no quizzes for knowledge test.
    Deep learning is currently a very popular research

    direction, the use of convolution neural network convolution

    layer, pool layer and the whole connection layer and other

    basic structure, you can let the network structure to learn and

    extract the relevant features, and to be used. This feature

    provides many conveniences for many studies, eliminating the

    need for a very complex modeling process.
  • Anonymous
    It was to helpful for me.. ........
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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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