[DeepBayes2019]: Day 5, Lecture 4. Variational inference with implicit and semi-implicit models Published 2019-08-31 Download video MP4 360p Recommendations 31:46 Denoising and Variational Autoencoders 16:28 SVD Visualized, Singular Value Decomposition explained | SEE Matrix , Chapter 3 #SoME2 1:40:04 Станислав Дробышевский: вопросно-ответная сессия 2:25:52 The spelled-out intro to neural networks and backpropagation: building micrograd 2:13:51 Олимпиадки, асинхронность и удалённая работа / Всё о Python / Интервью с Python Developer 3:57:35 Math for Game Devs [2022, part 1] • Numbers, Vectors & Dot Product 1:27:18 From GPT-3 to ChatGPT: Training Language Models on Instructions and Human Feedback [in Russian] 20:52 [DeepLearning | видео 2] Градиентный спуск: как учатся нейронные сети 2:17:09 Transformer, explained in detail | Igor Kotenkov | NLP Lecture (in Russian) 3:57:55 Learn TensorFlow and Deep Learning fundamentals with Python (code-first introduction) Part 2/2 3:55:27 Reinforcement Learning Course - Full Machine Learning Tutorial 3:39:20 Math for Game Devs [2022, part 7] • Interpolation & Point Physics 1:32:16 AI Alignment problem [in Russian] 3:06:44 Math for Game Devs [2022, part 3] • Matrix4x4 & Cross Product 3:58:45 Building the Formula 1 App with React Native 3:12:21 Адаптивная верстка сайта с нуля для начинающих. Объяснение действий. HTML CSS 1:11:29 (Doubly) Semi-Implicit Variational Inference 2:58:07 Math for Game Devs [2022, part 2] solutions for assignments 1-3 Similar videos 10:31 Semi Implicit Variational Inference Presentation 1:15:46 Variational Inference with Implicit Distributions 1:00:45 [DeepBayes2019]: Day 5, Lecture 3. Langevin dynamics for sampling and global optimization 1:10:45 Lecture 08 Variational Inference 2 1:24:23 Нейробайесовские методы. Семинар 7. Semi-Implicit Variational Inference (SIVI) 1:06:11 Нейробайесовские методы. Лекция 7. Semi-Implicit Variational Inference (SIVI) 05:52 [DeepBayes2019]: Day 6, Practical session 2. Sparsification of deep neural networks 13:04 Team 6. robust accurate stochastic optimization for variational inference 1:13:14 [DeepBayes2018]: Day 4, lecture 1. Generative models 1:16:56 Lecture 17 - Deep Generative Models: Overview and Connections 21:22 Debiasing Evidence Approximations: Importance-Weighted Autoencoders Jackknife Variational Inference More results