Deep Learning for Computer Vision - Justin Johnson

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superseded= {}

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