cs231n-CNNs (15 files)
 CS231n Winter 2016 - Lecture1 - Introduction and Historical Context-NfnWJUyUJYU.mp4 |
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| CS231n Winter 2016 - Lecture 9 - Visualization, Deep Dream, Neural Style, Adversarial Examples-ta5fdaqDT3M.mp4 |
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| CS231n Winter 2016 - Lecture 8 - Localization and Detection-GxZrEKZfW2o.mkv |
947.56MB |
| CS231n Winter 2016 - Lecture 7 - Convolutional Neural Networks-LxfUGhug-iQ.mkv |
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| CS231n Winter 2016 - Lecture 6 - Neural Networks Part 3 _ Intro to ConvNets-hd_KFJ5ktUc.mkv |
604.64MB |
| CS231n Winter 2016 - Lecture 5 - Neural Networks Part 2-gYpoJMlgyXA.mkv |
652.52MB |
| CS231n Winter 2016 - Lecture 4 - Backpropagation, Neural Networks 1-i94OvYb6noo.mkv |
610.01MB |
| CS231n Winter 2016 - Lecture 3 - Linear Classification 2, Optimization-qlLChbHhbg4.mkv |
706.90MB |
| CS231n Winter 2016 - Lecture 2 - Data-driven approach, kNN, Linear Classification 1-8inugqHkfvE.mkv |
541.39MB |
| CS231n Winter 2016 - Lecture 15 - Invited Talk by Jeff Dean-T7YkPWpwFD4.mkv |
597.73MB |
| CS231n Winter 2016 - Lecture 14 - Videos and Unsupervised Learning-ekyBklxwQMU.webm |
657.32MB |
| CS231n Winter 2016 - Lecture 13 - Segmentation, soft attention, spatial transformers-ByjaPdWXKJ4.webm |
529.53MB |
| CS231n Winter 2016 - Lecture 12 - Deep Learning libraries-Vf_-OkqbwPo.webm |
637.70MB |
| CS231n Winter 2016 - Lecture 11 - ConvNets in practice-pA4BsUK3oP4.webm |
574.89MB |
| CS231n Winter 2016 - Lecture 10 - Recurrent Neural Networks, Image Captioning, LSTM-yCC09vCHzF8.mkv |
542.54MB |
Type: Course
Bibtex:
Tags:
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= {}
}
