The Ultimate Guide to Hyperparameter Tuning | Grid Search vs. Randomized Search

#ai #ml #datascience #learnai #learning #artificialintelligence #machinelearning 🔥 Hyperparameters are the parameters of the model that are not learned during the training process but are set by the user before the process starts. They control the training phase and model behavior. Different machine learning models have different hyperparameters that can have a significant affect on the performance of the model. There are two common ways for hyperparameter search. Using grid search you define potential values for each hyperparameter and train a separate model for each set. This method is computationally expensive, because you train too many models as the number of hyperparameters and number of their values increases. Using Randomized Search, on the other hand, you provide range for each hyperparameter and how many times you want to train a model. The method then samples values from the range of each hyperparameter and trains separate models (as many as you specified). Both have their advantages and disadvantages. 🔍 Key points covered: 0:00 - What are the hyperparameters? 0:25 - Why are hyperparameters important? 0:35 - Example of a hyperparameter. 1:07 - But how to find the best hyperparameters? 1:26 - Grid Search. 2:09 - One major problem of grid search. 2:31 - Randomized Search. 3:04 - Which one to choose and when? 3:19 - What about large neural networks? 3:31 - Subscribe to us! 🔔 Don't forget to like, subscribe, and hit the bell icon to stay updated with our latest videos! 🤖 Note that we use synthetic generations, such as AI-generated images and voices, to enhance the appeal and engagement of our content. 🌐 If you have any questions or topics you want us to cover, leave a comment below. Additionally, share with your thoughts about the content, how do you think we can make them better? Thanks for watching!