CS231n: Convolutional Neural Networks Spring 2017
Stanford



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cs231n-spring2017 (16 files)
Lecture 1 _ Introduction to Convolutional Neural Networks for Visual Recognition-vT1JzLTH4G4.mp4152.68MB
Lecture 2 _ Image Classification-OoUX-nOEjG0.mp4134.76MB
Lecture 3 _ Loss Functions and Optimization-h7iBpEHGVNc.mp4169.43MB
Lecture 4 _ Introduction to Neural Networks-d14TUNcbn1k.mp4146.75MB
Lecture 5 _ Convolutional Neural Networks-bNb2fEVKeEo.mp4157.41MB
Lecture 6 _ Training Neural Networks I-wEoyxE0GP2M.mp4177.50MB
Lecture 7 _ Training Neural Networks II-_JB0AO7QxSA.mp4161.18MB
Lecture 8 _ Deep Learning Software-6SlgtELqOWc.mp4175.66MB
Lecture 9 _ CNN Architectures-DAOcjicFr1Y.mp4176.81MB
Lecture 10 _ Recurrent Neural Networks-6niqTuYFZLQ.mp4156.96MB
Lecture 11 _ Detection and Segmentation-nDPWywWRIRo.mp4171.19MB
Lecture 12 _ Visualizing and Understanding-6wcs6szJWMY.mp4187.59MB
Lecture 13 _ Generative Models-5WoItGTWV54.mp4169.06MB
Lecture 14 _ Deep Reinforcement Learning-lvoHnicueoE.mp4134.41MB
Lecture 15 _ Efficient Methods and Hardware for Deep Learning-eZdOkDtYMoo.mp4193.42MB
Lecture 16 _ Adversarial Examples and Adversarial Training-CIfsB_EYsVI.mp4160.34MB
Type: Course
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Bibtex:
@article{,
title= {CS231n: Convolutional Neural Networks Spring 2017},
keywords= {},
author= {Stanford},
abstract= {Stanford course on Convolutional Neural Networks for Visual Recognition

# Course Description

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 course 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. During the 10-week 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. The final assignment will involve training a multi-million parameter convolutional neural network and applying it on the largest image classification dataset (ImageNet). We will focus on teaching how to set up the problem of image recognition, the learning algorithms (e.g. backpropagation), practical engineering tricks for training and fine-tuning the networks and guide the students through hands-on assignments and a final course project. Much of the background and materials of this course will be drawn from the ImageNet Challenge.

https://i.imgur.com/ps0x3Wo.png},
terms= {},
license= {},
superseded= {},
url= {},
year= {2017}
}