Spearmint (hyperparameter optimization using Gaussian processes) and Hyperopt are two examples of smart hyper-parameters (hyperparameter optimization using Tree-based estimators).
The process of hyper parameter adjustment (optimization) is an important part of machine learning. A proper selection of hyperparameters might help a model achieve the intended metric value or, on the other hand, can lead to an endless cycle of training and optimization.
Grid search is the most fundamental way of hyperparameter tweaking. We simply generate a model for each conceivable combination of all of the hyperparameter values provided, evaluate each model, and choose the architecture that delivers the best results using this technique.
Hyperparameters are significant because they directly regulate the training algorithm's behaviour and have a major impact on the model's performance. "A well-chosen set of hyperparameters can make an algorithm really shine."
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Rishu Shrivastav
5
explained everything in detail. I have a question learnvern provide dataset , and ppt ? or not?
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VIKAS CHOUBEY
5
very nicely explained
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Vrushali Kandesar
5
Awesome and very nicely explained!!!
One importing thing to notify to team is by mistakenly navie's practical has been added under svm lecture and vice versa (Learning Practical 1)
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Mohd Mushraf
5
Amazing Teaching
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Juboraj Juboraj
5
Easy to understand & explain details.
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Joydeb
5
Awesome Course sir and your teaching style is very GOOD.
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Shaga Chandrakanth Goud
5
Hi Kushal ji, Thanks a lot for a very good explanation. I have doubts about where we can get the dataset that you explained in the video. Can you make it available in resource ,so that we can downld
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Neel Khairnar
5
Kushal is very good explainer he is covering all topics nicely 👍
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