Lecture 6 | Convergence, Loss Surfaces, and Optimization

Показать описание

Carnegie Mellon University
Course: 11-785, Intro to Deep Learning
Offering: Fall 2019

Contents:
• Convergence in neural networks
• Rates of convergence
• Loss surfaces
• Learning rates, and optimization methods
• RMSProp, Adagrad, Momentum

Carnegie Mellon University Deep Learning

Рекомендации по теме

Комментарии

If you are seeking for deep learning lectures from someone who truly understands the contents and makes connecting dots as well, here you go: Professor Raj is the best I've ever seen in deep learning. He is explaining the whole things with simple languages (very deep intuition).

mingx

Looks like this professor has become my hero for deep learning course. I started understanding his classes better than any one else's.

vipa

The first video u click, feeling encouraged and one of the first thing you hear from the Professor is the decline of student attending the class and the sinking feeling of hope, that it would be a miracle if there would be 1 student by the end of this course. Lol.
Still excellent content.

minatonamikazi

Kids run away 😂😂😂😂
That's what happens after neural networks
Back proporgation

Enem_Verse

this video didn't give theoretical guarantees for stochastic gradient descent

salmanfarhat

Lecture 6 | Convergence, Loss Surfaces, and Optimization

Lecture 6 | Convergence, Loss Surfaces, and Optimization

Lecture 6: Convergence issues, Loss Surfaces, Momentum

Lecture 6 | Convergence

Convergence — Lesson 6

[W2-5] convergence rate of strongly convex and smooth functions

MAT1841 - Lec 6 - More Markov Chains (incl. finishing convergence)

Neural networks [2.11] : Training neural networks - optimization

Why optimization convergence matters

Convergence time analysis of Asynchronous Distributed Artificial Neural Networks

Machine Learning 10-701 Lecture 15, Convergence bounds

Why You Are Using The RSI WRONG

22. Gradient Descent: Downhill to a Minimum

L26.9 Gambler's Ruin

Last-Iterate Convergence in Constrained Min-Max Optimization: SOS to the Rescue

Backpropagation, step-by-step | DL3

(Old) Lecture 5 | Convergence in Neural Networks

How to eat Roti #SSB #SSB Preparation #Defence #Army #Best Defence Academy #OLQ

Lecture 5 | Convergence, Learning Rates, and Gradient Descent

On the Global Convergence of Gradient Descent for (...) - Bach - Workshop 3 - CEB T1 2019

6 ROUTE 2 0 Routing Protocol Convergence

Diffraction Pattern of Light by Single Slit Using Two Blades....

Advance #Deep Learning, Concept of #Convex Function, Loss function, Convergence, Lecture 11

Deep Learning Lecture 6: Optimization

Stability and Convergence Trade-Off of Iterative Optimization Algorithms