vpoornima1322@gmail.com Activation Function Sigmoid Function Sigmoid is an activation function used in neural networks. It takes a real valued number and is converted into 0 and 1, this is mostly useful in decision or classification. Sigmoid function is defined as is the input, is the base of the natural logarithm approximately equal to 2.71828. … Read more
Backward Propagation is a fundamental algorithm used in training neural networks. It adjusts the weights to minimize the error between the actual output and the predicted output. Forward Propagation Before backward propagation, the network goes through the hidden layers, weighted sum is calculated at each layer and an activation function is applied to produce an … Read more
Forward Propagation Forward propagation is the process in a neural network to produce an output. It’s called “forward” because the data moves in one direction from input layer to the output layer. The input layer receives the raw data and is passed through the hidden layer. Each neuron in this layer computes the weighted sum … Read more
Perceptron Perceptron is a fundamental concept in deep learning and a building block in neural network introduced by Frank Rosenblatt in 1957. Structure Input: Perceptron takes multiple input values denoted as each corresponding to a feature in the input data. Weights: Each input is associated with a weight w. They represent the importance of the … Read more
