Implementing Dropout as a Bayesian Approximation in TensorFlow Published 2018-01-03 Download video MP4 360p Recommendations 36:15 Eric J. Ma - An Attempt At Demystifying Bayesian Deep Learning 07:41 Uncertainty in Neural Networks? Monte Carlo Dropout 35:42 Bayesian Deep learning with 10% of the weights - Rob Romijnders 17:07 LoRA explained (and a bit about precision and quantization) 1:57:07 Bayesian Deep Learning and Probabilistic Model Construction - ICML 2020 Tutorial 09:47 ResNet (actually) explained in under 10 minutes 15:05 Variational Autoencoders 48:52 MIT 6.S191: Evidential Deep Learning and Uncertainty 25:09 How Bayes Theorem works 31:33 The Oldest Unsolved Problem in Math 1:15:53 Yarin Gal -. Bayesian Deep Learning 12:29 Markdown vs AsciiDoc 07:03 Bayesian Neural Network | Deep Learning 12:52 Top 5 Beginner PCB Design Mistakes (and how to fix them) 20:00 The Quantum Hype Bubble Is About To Burst 16:22 Garbage Collection (Mark & Sweep) - Computerphile 43:40 Probabilistic Programming and Bayesian Modeling with PyMC3 - Christopher Fonnesbeck 39:02 Uncertainty Quantification and Deep Learning ǀ Elise Jennings, Argonne National Laboratory 29:00 Probabilistic Deep Learning in TensorFlow: The Why and How | ODSC Europe 2019 Similar videos 39:20 Andrew Rowan - Bayesian Deep Learning with Edward (and a trick using Dropout) 18:57 Tensorflow 17 Regularization dropout (neural network tutorials) 13:55 How to handle Uncertainty in Deep Learning #2.1 13:24 9.3: Using Dropout with Keras and TensorFLow (Module 9, Part 3) 12:01 86 Activity Deep Learning in the Tensorflow Playground 58:37 Scalable Bayesian Deep Learning with Modern Laplace Approximations 30:47 Uncertainty estimation and Bayesian Neural Networks - Marcin Możejko 14:30 Fast-BCNN: Massive Neuron Skipping in Bayesian Convolutional Neural Networks More results