From a51621da8aabd6601923b20eab801707713b5bef Mon Sep 17 00:00:00 2001 From: manishh12 Date: Fri, 24 May 2024 23:01:05 +0530 Subject: [PATCH] updated readme issue#527 --- contrib/machine-learning/Types_of_optimizers.md | 2 -- 1 file changed, 2 deletions(-) diff --git a/contrib/machine-learning/Types_of_optimizers.md b/contrib/machine-learning/Types_of_optimizers.md index 7d0a617..e941597 100644 --- a/contrib/machine-learning/Types_of_optimizers.md +++ b/contrib/machine-learning/Types_of_optimizers.md @@ -1,7 +1,5 @@ -Sure, here's a more detailed explanation for each optimizer, including the mathematical formulation, intuition, advantages, and disadvantages, along with the Python implementation. --- - # Optimizers in Machine Learning Optimizers are algorithms or methods used to change the attributes of your neural network such as weights and learning rate in order to reduce the losses. Optimization algorithms help to minimize (or maximize) an objective function (also called a loss function) which is simply a mathematical function dependent on the model's internal learnable parameters which are used in computing the target values from the set of features.