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The Vanishing Gradient Problem

Towards Data Science

The problem: as more layers using certain activation functions are added to neural networks, the gradients of the loss function approaches zero, making the network hard to train. Why: The sigmoid…

The Problem of Vanishing Gradients

Towards Data Science

Vanishing gradients occur while training deep neural networks using gradient-based optimization methods. It occurs due to the nature of the backpropagation algorithm that is used to train the neural…

Vanishing Gradient Problem in Deep Learning

Analytics Vidhya

In 1980’s, at that time the researches were not able to find deep neural network in ANN because we have to use sigmoid in each and every neuron as the ReLU was not invented. Because of sigmoid…

Vanishing and Exploding Gradients

Level Up Coding

In this blog, I will explain how a sigmoid activation can have both vanishing and exploding gradient problem. Vanishing and exploding gradients are one of the biggest problems that the neural network…...

Vanishing gradient in Deep Neural Network

Towards Data Science

Nowadays, the networks used for image analysis are made by many layers stacked one after the other to form so-called deep networks. One of the biggest problems in training these architectures is the…

Exploding And Vanishing Gradient Problem: Math Behind The Truth

Becoming Human: Artificial Intelligence Magazine

Hello Stardust! Today we’ll see mathematical reason behind exploding and vanishing gradient problem but first let’s understand the problem in a nutshell. “Usually, when we train a Deep model using…

Vanishing and Exploding Gradient Problems

Analytics Vidhya

One of the problems with training very deep neural network is that are vanishing and exploding gradients. (i.e When training a very deep neural network, sometimes derivatives becomes very very small…

How to Fix the Vanishing Gradients Problem Using the ReLU

MachineLearningMastery.com

Last Updated on August 25, 2020 The vanishing gradients problem is one example of unstable behavior that you may encounter when training a deep neural network. It describes the situation where a deep ...

Alleviating Gradient Issues

Towards Data Science

Solve Vanishing or Exploding Gradient problem while training a Neural Network using Gradient Descent by using ReLU, SELU, activation functions, BatchNormalization, Dropout & weight initialization

Visualizing the vanishing gradient problem

MachineLearningMastery.com

Last Updated on November 26, 2021 Deep learning was a recent invention. Partially, it is due to improved computation power that allows us to use more layers of perceptrons in a neural network. But at ...

More about The Gradient

Towards Data Science

In conversations with Eugene, I identified specific examples which may aid my most recent page, “Overcoming the Vanishing Gradient Problem”. Consider a recurrent neural network that plays a Maze…

Vanishing & Exploding Gradient Problem: Neural Networks 101

Towards Data Science

What are Vanishing & Exploding Gradients? In one of my previous posts, we explained neural networks learn through the backpropagation algorithm. The main idea is that we start on the output layer and ...