Deep learning and Neural networks expert-Deep Learning and Neural Networks Expert

Introduction to Deep Learning and Neural Networks

Deep learning and neural networks are a subset of machine learning that use multiple layers of nodes (neurons) to model complex patterns in data. Neural networks are designed to simulate the way the human brain processes information, enabling machines to recognize patterns, make decisions, and predict outcomes. A neural network consists of an input layer, one or more hidden layers, and an output layer. Each node in a layer is connected to nodes in the next layer with associated weights that adjust during training to minimize prediction error. An example to illustrate this is image recognition. Suppose you want to build a neural network to identify handwritten digits. The input layer would take pixel values of an image, hidden layers would process these values through multiple stages of transformation, and the output layer would provide the predicted digit. The network learns by adjusting weights based on errors in its predictions, improving accuracy over time.