A Neural Network Network is a AI computational model inspired by the way biological neural networks in the human brain function
It’s the backbone of most machine learning, particularly deep learning models. Key Components are
- Neurons (Nodes)
- A neural network consists of individual units called neurons or nodes. These neurons are structured in layers, and each neuron receives input, processes it, and passes an output to other neurons in subsequent layers.
- Layers of Neural Networks
Neural networks are composed of multiple layers:- Input Layer: This layer consists of neurons that receive the input features of the dataset. For example, in image processing, the input layer might receive pixel values.
- Hidden Layers: These are the layers between the input and output. A network can have multiple hidden layers, and this is what makes it “deep.” Neurons in these layers receive inputs from the previous layer, apply transformations using activation functions (explained later), and pass the results forward. The role of hidden layers is to learn complex patterns in the data.
- Output Layer: This layer provides the final prediction or classification. For instance, in a classification task, the output layer might consist of two nodes (one for each class) if it’s a binary classification task.
- Weights and Biases
- Each connection between two neurons in adjacent layers has a weight associated with it, which determines the importance of that connection. The weights are parameters that the network learns during training. Each neuron has a bias value added to the weighted sum of its inputs. The bias helps shift the activation function, which allows the network to better fit the data.
- Activation Functions
After computing the weighted sum of the inputs (plus the bias), the neuron applies an activation function to decide whether it should “activate” or not. The activation function introduces non-linearity into the model, allowing the network to learn complex patterns. Common activation functions include:- Sigmoid: Maps input to a value between 0 and 1. Commonly used in binary classification.
- ReLU (Rectified Linear Unit): Outputs the input directly if it’s positive, otherwise, it outputs zero. It’s commonly used in hidden layers because it helps deal with the vanishing gradient problem.
- Tanh: Maps input to a value between -1 and 1. It’s zero-centered, unlike sigmoid
- Softmax: Used in the output layer for multi-class classification tasks. It converts raw scores into probabilities that sum to 1.
- Forward Propagation
Inforward propagation, the network takes in input data and passes it through the layers. Each layer performs its calculations, applying weights, biases, and activation functions, until the output layer produces a prediction. This process is known as forward propagation because the data moves from the input layer through the hidden layers to the output layer.
- Loss Function
The output produced by the network is compared with the actual target output using a loss function. The loss function measures how well the neural network’s predictions match the actual labels. Common loss functions include:- Mean Squared Error (MSE): For regression tasks, measuring the squared difference between predicted and actual values.
- Cross-Entropy Loss: For classification tasks, especially multi-class classification, measuring how far the predicted probability distribution is from the actual distribution.
- Backpropagation and Gradient Descent
The key to learning in a neural network is backpropagation. Once the loss is computed, the network uses backpropagation to adjust the weights and biases to minimize the loss. Backpropagation works as follows:- The error (difference between prediction and actual output) is propagated backward from the output layer to the hidden layers.
- During this, the network computes the gradients of the loss with respect to each weight using the chain rule of calculus.
- Using these gradients, gradient descent is applied to update the weights and biases in the network to minimize the loss. The process of updating weights is done using a learning rate that controls how large a step the gradient descent takes at each iteration.
- Learning Rate
The learning rate is a key hyperparameter that controls how much the model’s weights are adjusted in response to the gradient. If the learning rate is too small, training will take a long time. If it’s too large, the model might converge too quickly to a suboptimal solution.
- Epochs and Iterations
Training a neural network involves going through the entire dataset multiple times, which is measured in epochs. Each epoch refers to one full cycle through the training data. During each epoch, the weights and biases are adjusted based on the gradient descent algorithm.
- Overfitting and Regularization
Neural networks can sometimes learn too well, fitting the training data perfectly but performing poorly on unseen data. This is called overfitting. To prevent this, techniques like regularization, dropout and early stopping are used.
- Types of Neural Networks
There are several types of neural networks, each suited for different tasks:- Feedforward Neural Networks (FNN): The simplest type, where the information flows in one direction—from input to output.
- Convolutional Neural Networks (CNN): Primarily used for image data. CNNs use convolutional layers to detect spatial hierarchies in images.
- Recurrent Neural Networks (RNN): Used for sequence data (e.g., time series or text), RNNs maintain information across steps in a sequence by having loops in their architecture.
- Transformer Networks: Popular in natural language processing (NLP) tasks, transformers use mechanisms like attention to handle long-range dependencies more effectively than RNNs.
- Applications of Neural Networks
Neural networks are used in many fields, including:- Image recognition(e.g., facial recognition, object detection)
- Natural language processing(e.g., machine translation, sentiment analysis)
- Speech recognition
- Autonomous vehicles
- Recommender systems