Deep Learning Lecture 3: Maximum likelihood and information

Показать описание

Course taught in 2015 at the University of Oxford by Nando de Freitas with great help from Brendan Shillingford.

Рекомендации по теме

Комментарии

Fantastic videos, I appreciate how open you're being with the coursework!

MadcowDeity

Out of the entire course this is easily the most important lecture. I've watched it several times to really internalize it.

mpete

Your energy is appreciable in this whole playlist!

osamamustafa

Thank you very much for posting these lectures! So far, I find them interesting and understandable.

paulthomann

very clear connection between least square loss, MLE and KL divergence. Thanks.

CW

This lecturer teaches more than my lecturer in 1 hour. So good. Please teach in my uni )):

dbskluvu

59:55 Why does the prediction for new data x_* calculate P(y|x_*, D, σ) instead of (x_*)^T Θ ?

aa-vbvh

Better explanation than Bishop's chapter 1 : )

dragonlorder

Good content for beginners ! I strongly advise against your method to simulate gaussian variables though. It is a general method (the use of the inverse of the distribution function) that is really bad for gaussian variables in terms of complexity. The two best way to go are Box-Müller's method and Marsaglia's method (and from my own experience their performance are equivalent). That being said, I enjoyed this video, thank you for sharing !

treflir

At the very end, how closely related is cross entropy to that KL/MLE relationship? It seems that you have it as the variable term in 1:12:08 but I'm not sure.

VictorChavesVVBC

At the end of each video, it would be great to include a "Summary for Practitioners"...that distills the theory to practice...the why and the what.

npabbisetty

I like the video series, but the audio needs work. The constant background buzz is really distracting.

cognitiveinstinct

The guy is not good with mathematics, losing logarithms, confusing probabilities and values of density funcitions. But thanks anyway.

maiiabakhova

Deep Learning Lecture 3: Maximum likelihood and information

Deep Learning Lecture 3: Maximum likelihood and information

Lesson 3: Practical Deep Learning for Coders 2022

Lecture 3: Linear Classifiers

Lecture 3 'k-nearest neighbors' -Cornell CS4780 SP17

But what is a neural network? | Chapter 1, Deep learning

Lecture 3: Dummy Q-learning (table)

Lecture 3: Recurrent Neural Networks (Full Stack Deep Learning - Spring 2021)

MIT Deep Learning Genomics - Lecture 3 - Convolutional Neural Networks CNNs (Spring 2020)

Lecture 13 | Backpropagation II | CMPS 497 Deep Learning | Fall 2024

Deep Learning Lecture 8: Modular back-propagation, logistic regression and Torch

Deep Learning Lecture 11: Max-margin learning, transfer and memory networks

10-601 Machine Learning Spring 2015 - Lecture 3

Lesson 3: Practical Deep Learning for Coders

Machine Learning for Physicists (Lecture 3): Training networks, Keras, Image recognition

DeepMind x UCL | Deep Learning Lectures | 3/12 | Convolutional Neural Networks for Image Recognition

Deep RL Bootcamp Lecture 3: Deep Q-Networks

RL Course by David Silver - Lecture 3: Planning by Dynamic Programming

Lecture 3 | Learning, Empirical Risk Minimization, and Optimization

Salsa Night in IIT Bombay #shorts #salsa #dance #iit #iitbombay #motivation #trending #viral #jee

Biggest mistake I do while recording| behind the scene | #jennyslectures

Convolutional Neural Networks | CNN | Kernel | Stride | Padding | Pooling | Flatten | Formula

Support Vector Machine (SVM) in 2 minutes

MIT 6.S191 (2023): Convolutional Neural Networks

IIT Bombay Lecture Hall | IIT Bombay Motivation | #shorts #ytshorts #iit