Deep Learning for Computer Vision - Justin Johnson

folder michigan-deep-learning-for-computer-vision-2020 (22 files)
fileLecture 1 - Introduction to Deep Learning for Computer Vision-dJYGatp4SvA.mp4 152.74MB
fileLecture 10 - Training Neural Networks I-lGbQlr1Ts7w.mp4 160.20MB
fileLecture 11 - Training Neural Networks II-WUazOtlti0g.mp4 201.15MB
fileLecture 12 - Recurrent Networks-dUzLD91Sj-o.mp4 187.66MB
fileLecture 13 - Attention-YAgjfMR9R_M.mp4 160.03MB
fileLecture 14 - Visualizing and Understanding-G1hGwHVykDU.mp4 208.89MB
fileLecture 15 - Object Detection-TB-fdISzpHQ.mp4 189.19MB
fileLecture 16 - Detection and Segmentation-9AyMR4IhSWQ.mp4 187.86MB
fileLecture 17 - 3D Vision-S1_nCdLUQQ8.mp4 171.36MB
fileLecture 18 - Videos-A9D6NXBJdwU.mp4 200.54MB
fileLecture 19 - Generative Models I-Q3HU2vEhD5Y.mp4 168.72MB
fileLecture 2 - Image Classification-0nqvO3AM2Vw.mp4 168.18MB
fileLecture 20 - Generative Models II-igP03FXZqgo.mp4 196.41MB
fileLecture 21 - Reinforcement Learning-Qex3XzcFKP4.mp4 166.63MB
fileLecture 22 - Conclusion-s3Ky_Ls4YSY.mp4 170.35MB
fileLecture 3 - Linear Classifiers-qcSEP17uKKY.mp4 180.09MB
fileLecture 4 - Optimization-YnQJTfbwBM8.mp4 147.95MB
fileLecture 5 - Neural Networks-g6InpdhUblE.mp4 131.26MB
fileLecture 6 - Backpropagation-dB-u77Y5a6A.mp4 186.00MB
fileLecture 7 - Convolutional Networks-ANyxBVxmdZ0.mp4 150.73MB
fileLecture 8 - CNN Architectures-XaZIlVrIO-Q.mp4 174.29MB
fileLecture 9 - Hardware and Software-oXPX8GIOiU4.mp4 156.48MB
Type: Course

title= {Deep Learning for Computer Vision - Justin Johnson},
journal= {},
author= {},
year= {},
url= {},
abstract= {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 and object detection. Recent developments in neural network approaches have greatly advanced the performance of these state-of-the-art visual recognition systems. This course is a deep dive into details of neural-network based deep learning methods for computer vision. During this 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. We will cover learning algorithms, neural network architectures, and practical engineering tricks for training and fine-tuning networks for visual recognition tasks.},
keywords= {},
terms= {},
license= {},
superseded= {}

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