Gradient Descent Explained Clearly | How Machine Learning Models Actually Learn скачать в хорошем качестве

Gradient Descent Explained Clearly | How Machine Learning Models Actually Learn

What is Gradient Descent — and how do Machine Learning models actually learn? In this video, we break down one of the most important algorithms in Machine Learning: Gradient Descent. You’ll understand: • What a loss function is • Why we need gradients • How models update parameters • What learning rate means • Why Gradient Descent finds the minimum error This is the algorithm behind: Linear Regression Neural Networks Deep Learning Almost every modern ML model If you've ever called .fit() and wondered what happens inside — this is it. This clip is taken from my Machine Learning Deep Dive Series, where we remove the black box from ML. 📚 In the Full Series, We Cover: Linear Regression intuition (y = mx + b) Mean Squared Error (MSE) Method of Least Squares (Closed Form) Gradient Descent visual explanation Regularization (Ridge, Lasso, Elastic Net) Model evaluation (MSE, R², p-value) CPU vs GPU training (PyTorch demo) 🚀 Who This Is For ML beginners Developers using scikit-learn Anyone learning Deep Learning Students who want real intuition If you want to truly understand how models train instead of treating ML as magic — this series is for you. Subscribe for more deep technical breakdowns. #MachineLearning #GradientDescent #DeepLearning #LinearRegression #NeuralNetworks #LossFunction #MSE #Backpropagation #SupervisedLearning #DataScience #AI #MLExplained Links: https://github.com/guptnava/youtube_d... https://colab.research.google.com/dri...