Momentum optimizers - Learn Data Science with Travis

Optimizers — Momentum and Nesterov momentum algorithms (Part 2)

Analytics Vidhya

Welcome to the second part on optimisers where we will be discussing momentum and Nesterov accelerated gradient. If you want a quick review of vanilla gradient descent algorithms and its variants…

Momentum, Adam’s optimizer and more

Becoming Human: Artificial Intelligence Magazine

If you’ve checked the jupyter notebook related to my article on learning rates, you’d know that it had an update function which was basically calculating the outputs, calculating the loss and…

Momentum: A simple, yet efficient optimizing technique

Analytics Vidhya

What are gradient descent, moving average and how can they be applied to optimize Neural Networks? How is Momentum better than gradient Descent?

Gradient Descent With Momentum from Scratch

Machine Learning Mastery

Last Updated on October 12, 2021 Gradient descent is an optimization algorithm that follows the negative gradient of an objective function in order to locate the minimum of the function. A problem wit...

Gradient Descent With Momentum

Towards Data Science

The problem with gradient descent is that the weight update at a moment (t) is governed by the learning rate and gradient at that moment only. It doesn’t take into account the past steps taken while…

Learning Parameters, Part 2: Momentum-Based And Nesterov Accelerated Gradient Descent

Towards Data Science

In this post, we look at how the gentle-surface limitation of Gradient Descent can be overcome using the concept of momentum to some extent. Make sure you check out my blog post — Learning…

Gradient Descent With Nesterov Momentum From Scratch

Machine Learning Mastery

Last Updated on October 12, 2021 Gradient descent is an optimization algorithm that follows the negative gradient of an objective function in order to locate the minimum of the function. A limitation ...

Stochastic Gradient Descent with momentum

Towards Data Science

This is part 2 of my series on optimization algorithms used for training neural networks and machine learning models. Part 1 was about Stochastic gradient descent. In this post I presume basic…

All About Stochastic Gradient Descent Extension- Nesterov momentum, the simple way!

Towards Data Science

Advance optimization techniques in Data Science with its simplified maths

A Bit Beyond Gradient Descent: Mini-Batch, Momentum, and Some Dude Named Yuri Nesterov

Towards Data Science

Last time, I discussed how gradient descent works on a linear regression model by coding it up in ten lines of python code. This was done in order to demonstrate the principles of gradient descent…

Optimizers Explained - Adam, Momentum and Stochastic Gradient Descent

Machine Learning From Scratch

Picking the right optimizer with the right parameters, can help you squeeze the last bit of accuracy out of your neural network model.

Momentum ,RMSprop And Adam Optimizer

Analytics Vidhya

Optimizer is a technique that we use to minimize the loss or increase the accuracy. We do that by finding the local minima of the cost function. When our cost function is convex in nature having only…...

Why to Optimize with Momentum

Analytics Vidhya

Momentum optimiser and its advantages over Gradient Descent

Optimizers

Machine Learning Glossary

Optimizers What is Optimizer ? It is very important to tweak the weights of the model during the training process, to make our predictions as correct and optimized as possible. But how exactly do you ...

Why 0.9? Towards Better Momentum Strategies in Deep Learning.

Towards Data Science

Momentum is a widely-used strategy for accelerating the convergence of gradient-based optimization techniques. Momentum was designed to speed up learning in directions of low curvature, without…

Momentum

Dive intro Deep Learning Book

In Section 12.4 we reviewed what happens when performing stochastic gradient descent, i.e., when performing optimization where only a noisy variant of the gradient is available. In particular, we noti...

Is PyTorch’s Nesterov Momentum Implementation Wrong?

Towards Data Science

Momentum helps SGD traverse complex loss landscapes more efficiently. Photo by Maxim Berg on Unsplash. Introduction If you look closely at PyTorch’s documentation of SGD, you will find that their impl...

Optimizers

Towards Data Science

In machine/deep learning main motive of optimizers is to reduce the cost/loss by updating weights, learning rates and biases and to improve model performance. Many people are already training neural…

An Intuitive and Visual Demonstration of Momentum in Machine Learning

Daily Dose of Data Science

Speedup machine learning model training with little effort.

Why Momentum Really Works

Distill

Here’s a popular story about momentum [1, 2, 3] : gradient descent is a man walking down a hill. He follows the steepest path downwards; his progress is slow, but steady. Momentum is a heavy ball rol...

Stochastic Gradient Descent & Momentum Explanation

Towards Data Science

Let’s talk about stochastic gradient descent(SGD), which is probably the second most famous gradient descent method we’ve heard most about. As we know, the traditional gradient descent method…

Optimizers — Gradient descent algorithms ( Part 1)

Analytics Vidhya

Hey everyone ! Welcome to my blog ! We are going to see the implementation of some of the basic optimiser algorithms in this blog. In machine learning, weights and biases are the learnable parameters…...

Optimizers for machine learning

Analytics Vidhya

In this we are going to learn optimizers which is the most important part of machine learning , in this blog I try to explain each and every concept of Optimizers in simple terms and visualization so…...

Gradient Descent Optimization With Nadam From Scratch

Machine Learning Mastery

Last Updated on October 12, 2021 Gradient descent is an optimization algorithm that follows the negative gradient of an objective function in order to locate the minimum of the function. A limitation ...