CS231n: Convolutional Neural Networks for Visual Recognition 2016
Fei-Fei Li and Andrej Karpathy and Justin Johnson



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cs231n-CNNs (15 files)
CS231n Winter 2016 - Lecture 10 - Recurrent Neural Networks, Image Captioning, LSTM-yCC09vCHzF8.mkv542.54MB
CS231n Winter 2016 - Lecture 11 - ConvNets in practice-pA4BsUK3oP4.webm574.89MB
CS231n Winter 2016 - Lecture 12 - Deep Learning libraries-Vf_-OkqbwPo.webm637.70MB
CS231n Winter 2016 - Lecture 13 - Segmentation, soft attention, spatial transformers-ByjaPdWXKJ4.webm529.53MB
CS231n Winter 2016 - Lecture 14 - Videos and Unsupervised Learning-ekyBklxwQMU.webm657.32MB
CS231n Winter 2016 - Lecture 15 - Invited Talk by Jeff Dean-T7YkPWpwFD4.mkv597.73MB
CS231n Winter 2016 - Lecture 2 - Data-driven approach, kNN, Linear Classification 1-8inugqHkfvE.mkv541.39MB
CS231n Winter 2016 - Lecture 3 - Linear Classification 2, Optimization-qlLChbHhbg4.mkv706.90MB
CS231n Winter 2016 - Lecture 4 - Backpropagation, Neural Networks 1-i94OvYb6noo.mkv610.01MB
CS231n Winter 2016 - Lecture 5 - Neural Networks Part 2-gYpoJMlgyXA.mkv652.52MB
CS231n Winter 2016 - Lecture 6 - Neural Networks Part 3 _ Intro to ConvNets-hd_KFJ5ktUc.mkv604.64MB
CS231n Winter 2016 - Lecture 7 - Convolutional Neural Networks-LxfUGhug-iQ.mkv724.23MB
CS231n Winter 2016 - Lecture 8 - Localization and Detection-GxZrEKZfW2o.mkv947.56MB
CS231n Winter 2016 - Lecture 9 - Visualization, Deep Dream, Neural Style, Adversarial Examples-ta5fdaqDT3M.mp41.23GB
CS231n Winter 2016 - Lecture1 - Introduction and Historical Context-NfnWJUyUJYU.mp41.17GB
Type: Course
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Bibtex:
@article{,
title= {CS231n: Convolutional Neural Networks for Visual Recognition 2016},
keywords= {},
journal= {},
author= {Fei-Fei Li and Andrej Karpathy and Justin Johnson},
year= {},
url= {http://cs231n.stanford.edu/},
license= {},
abstract= {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.},
superseded= {http://academictorrents.com/details/ed8a16ebb346e14119a03371665306609e485f13},
terms= {}
}