Lecture 9.4 — Introduction to the full Bayesian approach [Neural Networks for Machine Learning]
Lecture 9.4 — Introduction to the full Bayesian approach [Neural Networks for Machine Learning]
|
Yükleniyor...
Lütfen bekleyiniz...
Type
Size

 İlgili Videolar

Lecture 9.4 — Introduction to the full Bayesian approach [Neural Networks for Machine Learning]

Lecture 9.4 — Introduction to the full Bayesian approach [Neural Networks for Machine Learning]

|
Lecture 10.3 — The idea of full Bayesian learning [Neural Networks for Machine Learning]

Lecture 10.3 — The idea of full Bayesian learning [Neural Networks for Machine Learning]

|
Lecture 10.4 — Making full Bayesian learning practical [Neural Networks for Machine Learning]

Lecture 10.4 — Making full Bayesian learning practical [Neural Networks for Machine Learning]

|
Bayesian Learning based Gaussian Approximation for Artificial Neural Networks

Bayesian Learning based Gaussian Approximation for Artificial Neural Networks

|
Bayesian Deep Learning | NeurIPS 2019

Bayesian Deep Learning | NeurIPS 2019

|
Lecture 9.1 — Overview of ways to improve generalization [Neural Networks for Machine Learning]

Lecture 9.1 — Overview of ways to improve generalization [Neural Networks for Machine Learning]

|
10.1 — Why it helps to combine models [Neural Networks for Machine Learning]

10.1 — Why it helps to combine models [Neural Networks for Machine Learning]

|
History of Bayesian Neural Networks (Keynote talk)

History of Bayesian Neural Networks (Keynote talk)

|
Lecture 6.4 — Adaptive learning rates for each connection [Neural Networks for Machine Learning]

Lecture 6.4 — Adaptive learning rates for each connection [Neural Networks for Machine Learning]

|
Lecture 9.5 — The Bayesian interpretation of weight decay [Neural Networks for Machine Learning]

Lecture 9.5 — The Bayesian interpretation of weight decay [Neural Networks for Machine Learning]

|
[Project 2-min intro video] Bayesian Dark Knowledge

[Project 2-min intro video] Bayesian Dark Knowledge

|
Lecture 9.6 — MacKay 's quick and dirty method [Neural Networks for Machine Learning]

Lecture 9.6 — MacKay 's quick and dirty method [Neural Networks for Machine Learning]

|
Bayesian Generative Adversarial Networks

Bayesian Generative Adversarial Networks

|
MIT 6.S191: Uncertainty in Deep Learning

MIT 6.S191: Uncertainty in Deep Learning

|
Lecture 11.4 — Using stochastic units to improve search [Neural Networks for Machine Learning]

Lecture 11.4 — Using stochastic units to improve search [Neural Networks for Machine Learning]

|
Lecture 9.3 — Using noise as a regularizer [Neural Networks for Machine Learning]

Lecture 9.3 — Using noise as a regularizer [Neural Networks for Machine Learning]

|
Yee Whye Teh: On Bayesian Deep Learning and Deep Bayesian Learning (NIPS 2017 Keynote)

Yee Whye Teh: On Bayesian Deep Learning and Deep Bayesian Learning (NIPS 2017 Keynote)

|
Lecture 12.3 — Restricted Boltzmann Machines [Neural Networks for Machine Learning]

Lecture 12.3 — Restricted Boltzmann Machines [Neural Networks for Machine Learning]

|
scale.bythebay.io: Alexander Tsyplikhin, Introduction To Deep Learning

scale.bythebay.io: Alexander Tsyplikhin, Introduction To Deep Learning

|
Uncertainty - Lecture 2 - CS50's Introduction to Artificial Intelligence with Python 2020

Uncertainty - Lecture 2 - CS50's Introduction to Artificial Intelligence with Python 2020

|
Copyright. All rights reserved © 2025
Rosebank, Johannesburg, South Africa