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Stanford University

Convolutional Neural Networks for Visual Recognition (Spring 2017)

Stanford University via YouTube


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.


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

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