A neural network is a computational model inspired by the human brain. Just like our brain has billions of interconnected neurons, artificial neural networks consist of artificial neurons organized in layers. These networks can learn patterns from data and make predictions or decisions.
Each artificial neuron works like a simple calculator. It receives multiple inputs, multiplies each input by a specific weight, adds them all together along with a bias value, and then applies an activation function. The activation function, like ReLU, determines the final output of the neuron.
The forward pass is how a neural network processes information. Data enters through the input layer, flows through hidden layers where calculations happen, and finally reaches the output layer. Each neuron processes information from the previous layer and passes its result forward. This creates a flow of information from input to output.
Neural networks learn by comparing their predictions with the correct answers. When there's an error, the network uses backpropagation to adjust the weights. This process repeats many times, gradually reducing the error and improving accuracy. Through this iterative learning, the network becomes better at making predictions.
Neural networks have revolutionized many fields. They power image recognition in smartphones, language translation services, medical diagnosis systems, and autonomous vehicles. They excel at finding complex patterns in data that traditional programming cannot handle. From game-playing AI like AlphaGo to recommendation systems, neural networks are transforming how we interact with technology and solve complex problems.