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

folder cs231n-CNNs (15 files)
fileCS231n Winter 2016 - Lecture1 - Introduction and Historical Context-NfnWJUyUJYU.mp4 1.17GB
fileCS231n Winter 2016 - Lecture 9 - Visualization, Deep Dream, Neural Style, Adversarial Examples-ta5fdaqDT3M.mp4 1.23GB
fileCS231n Winter 2016 - Lecture 8 - Localization and Detection-GxZrEKZfW2o.mkv 947.56MB
fileCS231n Winter 2016 - Lecture 7 - Convolutional Neural Networks-LxfUGhug-iQ.mkv 724.23MB
fileCS231n Winter 2016 - Lecture 6 - Neural Networks Part 3 _ Intro to ConvNets-hd_KFJ5ktUc.mkv 604.64MB
fileCS231n Winter 2016 - Lecture 5 - Neural Networks Part 2-gYpoJMlgyXA.mkv 652.52MB
fileCS231n Winter 2016 - Lecture 4 - Backpropagation, Neural Networks 1-i94OvYb6noo.mkv 610.01MB
fileCS231n Winter 2016 - Lecture 3 - Linear Classification 2, Optimization-qlLChbHhbg4.mkv 706.90MB
fileCS231n Winter 2016 - Lecture 2 - Data-driven approach, kNN, Linear Classification 1-8inugqHkfvE.mkv 541.39MB
fileCS231n Winter 2016 - Lecture 15 - Invited Talk by Jeff Dean-T7YkPWpwFD4.mkv 597.73MB
fileCS231n Winter 2016 - Lecture 14 - Videos and Unsupervised Learning-ekyBklxwQMU.webm 657.32MB
fileCS231n Winter 2016 - Lecture 13 - Segmentation, soft attention, spatial transformers-ByjaPdWXKJ4.webm 529.53MB
fileCS231n Winter 2016 - Lecture 12 - Deep Learning libraries-Vf_-OkqbwPo.webm 637.70MB
fileCS231n Winter 2016 - Lecture 11 - ConvNets in practice-pA4BsUK3oP4.webm 574.89MB
fileCS231n Winter 2016 - Lecture 10 - Recurrent Neural Networks, Image Captioning, LSTM-yCC09vCHzF8.mkv 542.54MB
Type: Course

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= {}

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