In contrast, using the same datasets and boosting algorithms, our experiments show the opposite to be true when using neural networks (both CNNs and multilayer perceptrons (MLPs)). We find that a single neural network usually generalizes better than a boosted ensemble of smaller neural networks with the same total number of parameters.

Done

Done

Great way of teaching sir thanks.

I think these models can perform well but this process is little bit slow as compared to neural networks

In this lecture, the ensemble algorithm continued with boosting technique with its methods like Adaptive Boosting (AdaBoost), Gradient Boosting, Xtream Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), Categorical Boosting (CatBoost), Stochastic Gradient Boosting (SGB), Linear Programming Boost (LPBoost), and TotalBoost.

I learned in this lecture the types of Boosting algorithms AdaBoost (Adaptive Boosting), Gradient Boosting, CatBoost (Categorical Boosting), Stochastic Gradient Boosting, LPBoost (Linear Programming Boosting, TotalBoost (Total Boosting), and Light GBM 8-XGBoost (eXtreme Gradient Boosting).

I learned in this video the types of Boosting Algorithms ( 1-AdaBoost (Adaptive Boosting), 2-Gradient Boosting, 3-CatBoost (Categorical Boosting, 4-Stochastic Gradient Boosting, 5-LPBoost (Linear Programming Boosting), 6-TotalBoost (Total Boosting), 7-Light GBM, and 8-XGBoos).

AOA,I learned in this lecture about types of Boosting algorithms, which are1-AdaBoost (Adaptive Boosting) 2-Gradient Boosting 3-CatBoost (Categorical Boosting) 4-Stochastic Gradient Boosting 5-LPBoost (Linear Programming Boosting) 6-TotalBoost (Total Boosting) 7-Light GBM 8-XGBoost (eXtreme Gradiant Boosting) ALLAH PAK aap ko sahat o aafiat wali lambi umar ata kray aor ap ko dono jahan ki bhalian naseeb farmaey or ap k walid-e-mohtram ko karwat karwat jannat ata farmay,Ameen.