Navaneeth SharmaTransformers (Quick Revision)Self-Attention and Transformers RevisionJul 22, 2022Jul 22, 2022
Navaneeth SharmaNeural Networks Hyper-parameters (Quick Revision)-2Optimization Functions RevisionDec 10, 2021Dec 10, 2021
Navaneeth SharmaNeural Networks Hyper-parameters (Quick Revision)-1Activation Functions RevisionNov 6, 2021Nov 6, 2021
Navaneeth SharmaBoosting Techniques-2(Quick Revision)Gradient Boosting Decision Trees RevisionOct 30, 2021Oct 30, 2021
Navaneeth SharmaBoosting Techniques-1(Quick Revision)AdaBoost Algorithm RevisionOct 22, 2021Oct 22, 2021
Navaneeth SharmaBagging Techniques (Quick Revision)Random Forest Algorithm and Feature Extraction via BaggingOct 17, 2021Oct 17, 2021
Navaneeth SharmaBagging & Boosting Ensemble Models(Quick Revision)Quickly Going through Bagging and Boosting Techniques used in Tree based ModelsOct 16, 2021Oct 16, 2021
Navaneeth SharmaClassification Losses (Quick Revision)Cross Entropy Loss and Hinge Loss RevisionOct 10, 2021Oct 10, 2021
Navaneeth SharmaRegression Losses-2 (Quick Revision)Huber Loss and Adaptive Loss Revision (Some Explanation)Oct 9, 2021Oct 9, 2021
Navaneeth SharmaRegression Losses-1 (Quick Revision)Mean Squared Error (MSE) Loss Function and Mean Absolute Error (MAE) RevisionOct 5, 2021Oct 5, 2021
InAnalytics VidhyabyNavaneeth SharmaHow weights are initialized in Neural networks (Quick Revision)The Key factor which changed the trajectory of Deep LearningOct 4, 2021Oct 4, 2021
Navaneeth SharmaLasso | Ridge Regression (Quick Revision)The Must Learn Regression TechniquesOct 2, 2021Oct 2, 2021
