In this Webinar we are going to learn about, A Neural Network is a computational model mimicking the human brain, designed for tasks like pattern recognition. It comprises layers of nodes (neurons): input, hidden, and output. Neurons use weights and biases to process inputs and pass them through an activation function for output. The network learns by adjusting weights via backpropagation to minimize errors. Common applications include image recognition, natural language processing, and predictive analytics.

• Structure: Composed of layers — input, hidden, and output — with interconnected nodes (neurons).
• Input Layer: Receives raw data features (e.g., pixel values of an image).
• Hidden Layers: Perform computations using weighted connections and activation functions (e.g., ReLU, Sigmoid) to capture patterns and features.
• Output Layer: Produces the final output (e.g., classification label or prediction value).
• Learning Process: Uses backpropagation to adjust weights, minimizing the error through gradient descent.
• Activation Functions: Non-linear functions like Sigmoid, Tanh, and ReLU help capture complex relationships.