Description: Mini-batch Gradient Descent is a technique used in training machine learning models that combines the advantages of stochastic gradient descent and full-batch gradient descent. Instead of using the entire dataset to compute the gradient and update the model weights, this variant divides the dataset into small batches or ‘mini-batches’. Each mini-batch is used to compute the gradient and update the weights, allowing for faster and more efficient convergence. This technique not only reduces computation time but also introduces an element of randomness that can help avoid overfitting and improve model generalization. Additionally, using mini-batches allows for better utilization of computational resources, facilitating parallel processing and optimizing resource use. In summary, Mini-batch Gradient Descent is a key strategy in training deep learning models, allowing a balance between the stability of full-batch gradient descent and the speed of stochastic gradient descent.<\/p>\n","protected":false},"excerpt":{"rendered":"
Description: Mini-batch Gradient Descent is a technique used in training machine learning models that combines the advantages of stochastic gradient descent and full-batch gradient descent. Instead of using the entire dataset to compute the gradient and update the model weights, this variant divides the dataset into small batches or ‘mini-batches’. Each mini-batch is used to […]<\/p>\n","protected":false},"author":1,"featured_media":0,"menu_order":0,"comment_status":"open","ping_status":"open","template":"","meta":{"footnotes":""},"glossary-categories":[12130],"glossary-tags":[13086],"glossary-languages":[],"class_list":["post-257317","glossary","type-glossary","status-publish","hentry","glossary-categories-deep-learning-en","glossary-tags-deep-learning-en"],"post_title":"Minibatch Gradient Descent ","post_content":"Description: Mini-batch Gradient Descent is a technique used in training machine learning models that combines the advantages of stochastic gradient descent and full-batch gradient descent. Instead of using the entire dataset to compute the gradient and update the model weights, this variant divides the dataset into small batches or 'mini-batches'. Each mini-batch is used to compute the gradient and update the weights, allowing for faster and more efficient convergence. This technique not only reduces computation time but also introduces an element of randomness that can help avoid overfitting and improve model generalization. Additionally, using mini-batches allows for better utilization of computational resources, facilitating parallel processing and optimizing resource use. In summary, Mini-batch Gradient Descent is a key strategy in training deep learning models, allowing a balance between the stability of full-batch gradient descent and the speed of stochastic gradient descent.","yoast_head":"\n