Understanding Neural Networks: Softmax, Cross-Entropy, and Backpropagation Explained

Title: Understanding Neural Networks: Softmax, Cross-Entropy, and Backpropagation Explained
TeacherOn Profile: http://teacheron.com/tutor/2uQ6
Description:
In this video, we break down the fundamental concepts behind neural networks, focusing on the Softmax activation function, Cross-Entropy Loss, and Backpropagation. Whether you're new to deep learning or looking to refresh your understanding, this tutorial will walk you through each step of the forward pass and backpropagation process.

🧠 Topics Covered:

Input Layer - How data enters the network.

Hidden Layer - Computing activations using weighted sums and activations.

Softmax Activation - Converting the final outputs into probabilities.

Cross-Entropy Loss - Measuring the performance of the model.

Backpropagation - Deriving gradients and updating the weights using the chain rule.

💡 Who this video is for:

Beginners in neural networks or deep learning.

Those looking for a clear and intuitive understanding of how networks learn.

Data science enthusiasts who want to grasp backpropagation and loss functions in neural nets.

Видео Understanding Neural Networks: Softmax, Cross-Entropy, and Backpropagation Explained канала PostNetwork Academy