CS231n: Convolutional Neural Networks Spring 2017

folder cs231n-spring2017 (16 files)
fileLecture 1 _ Introduction to Convolutional Neural Networks for Visual Recognition-vT1JzLTH4G4.mp4 152.68MB
fileLecture 2 _ Image Classification-OoUX-nOEjG0.mp4 134.76MB
fileLecture 3 _ Loss Functions and Optimization-h7iBpEHGVNc.mp4 169.43MB
fileLecture 4 _ Introduction to Neural Networks-d14TUNcbn1k.mp4 146.75MB
fileLecture 5 _ Convolutional Neural Networks-bNb2fEVKeEo.mp4 157.41MB
fileLecture 6 _ Training Neural Networks I-wEoyxE0GP2M.mp4 177.50MB
fileLecture 7 _ Training Neural Networks II-_JB0AO7QxSA.mp4 161.18MB
fileLecture 8 _ Deep Learning Software-6SlgtELqOWc.mp4 175.66MB
fileLecture 9 _ CNN Architectures-DAOcjicFr1Y.mp4 176.81MB
fileLecture 10 _ Recurrent Neural Networks-6niqTuYFZLQ.mp4 156.96MB
fileLecture 11 _ Detection and Segmentation-nDPWywWRIRo.mp4 171.19MB
fileLecture 12 _ Visualizing and Understanding-6wcs6szJWMY.mp4 187.59MB
fileLecture 13 _ Generative Models-5WoItGTWV54.mp4 169.06MB
fileLecture 14 _ Deep Reinforcement Learning-lvoHnicueoE.mp4 134.41MB
fileLecture 15 _ Efficient Methods and Hardware for Deep Learning-eZdOkDtYMoo.mp4 193.42MB
fileLecture 16 _ Adversarial Examples and Adversarial Training-CIfsB_EYsVI.mp4 160.34MB
Type: Course

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}

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