Explanation of back propagation and its importance in neural networks.

Overview for newcomers to neural networks.

Neural networks consist of input, hidden, and output layers.

Neurons are interconnected across these layers.

Input data is processed through layers using weights, biases, and activation functions.

Understanding the roles of weights, activation functions, and biases.

Back propagation helps the network learn from errors.

Definition of loss function and how errors are calculated.

Gradient descent optimizes weights and biases to minimize error.

Iterative adjustments improve accuracy in subsequent cycles.

Example of speech recognition to illustrate error correction.

Case study of improving outputs through training.

Difference between static back propagation in feed-forward networks and recurrent networks.

Use cases, including OCR and sentiment analysis.

Summary of back propagation's critical role in neural network learning.

Iterative process of error correction enhances performance.