Скачать или смотреть Regularization & Optimization in Deep Learning Explained Simply | Overfitting to Best Accuracy

In this video, we explain Regularization and Optimization techniques in Deep Learning in a simple and easy way.
You’ll learn how deep learning models avoid overfitting, improve generalization, and achieve better accuracy using techniques like L1, L2 regularization, Dropout, Batch Normalization, and optimization algorithms such as Gradient Descent, Adam, RMSprop, and SGD.

This topic is very important for:

AI & ML students

Deep Learning beginners

Exam preparation

Interview preparation

Watch till the end to understand how optimization algorithms speed up learning and how regularization improves model performance.

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Regularization in deep learning
Optimization in deep learning
L1 regularization
L2 regularization
Dropout technique
Batch normalization
Gradient descent
Stochastic gradient descent
Adam optimizer
RMSprop optimizer
Avoid overfitting in neural networks
Deep learning tutorial
Machine learning concepts
AI exam preparation
Deep learning optimization techniques