GitHub is where people build software. More than 150 million people use GitHub to discover, fork, and contribute to over 420 million projects.
GitHub is where people build software. More than 150 million people use GitHub to discover, fork, and contribute to over 420 million projects.
Advanced Deep Learning with TensorFlow 2 and Keras, Second Edition is a completely updated edition of the bestselling guide to the advanced deep learning techniques available today. Revised for TensorFlow 2.x, this edition introduces you to the practical side of deep learning with new chapters on ...
This is term project for AI618 course Generative models and unsupervised learning. - GitHub - TruongKhang/cLODE: This is term project for AI618 course Generative models and unsupervised learning.
In unsupervised learning we do not observe Y for any of our training examples. We simply have a data set that contains the features (X matrix). How can we learn anything in this case? What can we infer from patterns in the X's? These are the fundamental questions of unsupervised ...
Unsupervised Learning of Depth and Ego-Motion from Video Tinghui Zhou,Matthew Brown,Noah Snavely,David G. Lowe In CVPR 2017 (Oral). See theproject webpagefor more details. Please contact Tinghui Zhou (tinghuiz@berkeley.edu) if you have any questions. ...
I recommand you play with the hyper-parameters to find a regime where the visualisations are good. For example with the pre-trained deepcluster AlexNet, for conv1 using a learning rate of 3 and 30.000 iterations works well. For conv5, using a learning rate of 30 and 3.000 iterations give...
Unsupervised Learning for Image Registration. Contribute to voxelmorph/voxelmorph development by creating an account on GitHub.
Split-Brain Autoencoders: Unsupervised Learning by Cross-Channel Prediction. In CVPR, 2017. - richzhang/splitbrainauto
On Mon, Jan 27, 2014 at 8:46 AM, aravindhm notifications@github.com wrote: Is there an easy way to implement L1 regularization on the weight matrix of a fully connected network. Similarly I want to penalize the L1 norm of features in each layer. What is the best way to do that usin...