What is neural network | Feedforward neural network explained with mathematics behind it..

Feedforward neural networks, such as perceptrons and multilayer perceptrons (MLPs), process data sequentially from input to output. Perceptrons, the basic units, compute a weighted sum of inputs and apply an activation function to produce an output. While single-layer perceptrons are limited to linearly separable problems, MLPs extend their capabilities by introducing hidden layers for nonlinear transformations. These hidden layers consist of interconnected perceptrons, and through forward propagation, input data is passed layer by layer, enabling the network to learn complex patterns. During training, weights and biases are adjusted to minimize a defined loss function using techniques like backpropagation, which efficiently computes gradients for weight updates. MLPs possess the ability to approximate any continuous function with appropriate architecture, making them versatile across various domains. However, their effective utilization requires careful design, regularization, and tuning to prevent overfitting and ensure optimal performance. Despite their complexities, MLPs offer powerful learning capabilities and remain prevalent in modern machine learning applications.


Timestamps
00:20 - what is biological neural network and how it relate to ANN
01:55 - how single layer perceptron neural network works
03:25 - how multilayer perceptron neural network works
05:45 - how feedforward operation happen in neural network
06:57 - feedforward neural network operation explained mathematically
09:59 - mathematics behind neural network explained