Deep Learning (DL) is a subfield of machine learning (ML) focused on algorithms inspired by the structure and function of the brain, known as artificial neural networks. Deep learning models are designed to automatically learn patterns and representations from large datasets, making them highly effective in tasks like image recognition, natural language processing (NLP), speech recognition, and more. Here’s a detailed breakdown of deep learning concepts tailored for BTech students:
Neuron (Node) and Activation Function:
A neuron (or node) in a neural network is a basic unit that takes an input, processes it, and passes the result to the next layer.
The output of a neuron is determined by an activation function, which introduces non-linearity. Common activation functions include:
Sigmoid: Used for binary classification, outputs values between 0 and 1.
ReLU (Rectified Linear Unit): Introduces non-linearity and is widely used in hidden layers due to its simplicity and efficiency.
Tanh: Outputs values between -1 and 1.
Layers in a Neural Network : Input Layer: The layer that receives the raw data (features). For example, in image processing, the input could be pixel values.
Hidden Layers: Intermediate layers between input and output, where computations take place and features are learned.
Output Layer: The final layer that provides the prediction or output, such as classification labels or regression values.
