Lec :06 | Loss function & types in neural network | Mean squared error | mean absolute error | #ml

In this video, we’ll explain one of the most important concepts in training a neural network — the Loss Function. Understanding how loss functions work is key to building accurate and reliable machine learning and deep learning models. 📌 What You’ll Learn: What is a Loss Function and why it’s important The difference between Loss and Cost Common types of loss functions: Mean Squared Error (MSE) – For regression tasks Cross-Entropy Loss – For classification tasks Hinge Loss, Huber Loss, and more How a loss function helps in backpropagation and weight updates Visual explanation with simple real-life examples By the end of this video, you’ll clearly understand how loss functions guide neural networks to learn from mistakes and improve accuracy. 🚀 🔔 Subscribe for more tutorials on Neural Networks, Deep Learning, and AI. Don’t forget to like and share if you found it helpful! #neuralnetworks #lossfunction #deeplearning #machinelearning #AI #backpropagation #MSE #Crossentropy #learnai #ai