Name: CS231n: Convolutional Neural Networks Spring 2017
Creator: Stanford
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Path	Size
Lecture 1 _ Introduction to Convolutional Neural Networks for Visual Recognition-vT1JzLTH4G4.mp4	152.68MB
Lecture 2 _ Image Classification-OoUX-nOEjG0.mp4	134.76MB
Lecture 3 _ Loss Functions and Optimization-h7iBpEHGVNc.mp4	169.43MB
Lecture 4 _ Introduction to Neural Networks-d14TUNcbn1k.mp4	146.75MB
Lecture 5 _ Convolutional Neural Networks-bNb2fEVKeEo.mp4	157.41MB
Lecture 6 _ Training Neural Networks I-wEoyxE0GP2M.mp4	177.50MB
Lecture 7 _ Training Neural Networks II-_JB0AO7QxSA.mp4	161.18MB
Lecture 8 _ Deep Learning Software-6SlgtELqOWc.mp4	175.66MB
Lecture 9 _ CNN Architectures-DAOcjicFr1Y.mp4	176.81MB
Lecture 10 _ Recurrent Neural Networks-6niqTuYFZLQ.mp4	156.96MB
Lecture 11 _ Detection and Segmentation-nDPWywWRIRo.mp4	171.19MB
Lecture 12 _ Visualizing and Understanding-6wcs6szJWMY.mp4	187.59MB
Lecture 13 _ Generative Models-5WoItGTWV54.mp4	169.06MB
Lecture 14 _ Deep Reinforcement Learning-lvoHnicueoE.mp4	134.41MB
Lecture 15 _ Efficient Methods and Hardware for Deep Learning-eZdOkDtYMoo.mp4	193.42MB
Lecture 16 _ Adversarial Examples and Adversarial Training-CIfsB_EYsVI.mp4	160.34MB

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cs231n-spring2017 (16 files)

Lecture 1 _ Introduction to Convolutional Neural Networks for Visual Recognition-vT1JzLTH4G4.mp4	152.68MB
Lecture 2 _ Image Classification-OoUX-nOEjG0.mp4	134.76MB
Lecture 3 _ Loss Functions and Optimization-h7iBpEHGVNc.mp4	169.43MB
Lecture 4 _ Introduction to Neural Networks-d14TUNcbn1k.mp4	146.75MB
Lecture 5 _ Convolutional Neural Networks-bNb2fEVKeEo.mp4	157.41MB
Lecture 6 _ Training Neural Networks I-wEoyxE0GP2M.mp4	177.50MB
Lecture 7 _ Training Neural Networks II-_JB0AO7QxSA.mp4	161.18MB
Lecture 8 _ Deep Learning Software-6SlgtELqOWc.mp4	175.66MB
Lecture 9 _ CNN Architectures-DAOcjicFr1Y.mp4	176.81MB
Lecture 10 _ Recurrent Neural Networks-6niqTuYFZLQ.mp4	156.96MB
Lecture 11 _ Detection and Segmentation-nDPWywWRIRo.mp4	171.19MB
Lecture 12 _ Visualizing and Understanding-6wcs6szJWMY.mp4	187.59MB
Lecture 13 _ Generative Models-5WoItGTWV54.mp4	169.06MB
Lecture 14 _ Deep Reinforcement Learning-lvoHnicueoE.mp4	134.41MB
Lecture 15 _ Efficient Methods and Hardware for Deep Learning-eZdOkDtYMoo.mp4	193.42MB
Lecture 16 _ Adversarial Examples and Adversarial Training-CIfsB_EYsVI.mp4	160.34MB

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}
}

CS231n: Convolutional Neural Networks Spring 2017 Stanford

CS231n: Convolutional Neural Networks Spring 2017
Stanford