Artificial Neural Networks (ANNs)

## **1. Neural Network Basics** - **Perceptrons**: - **Description**: A perceptron is the most basic unit of a neural network, mimicking the behavior of a biological neuron. It takes in multiple inputs, applies weights to them, sums them up, and then applies an activation function to determine the output. - **How It Works**: The perceptron applies a linear combination of inputs and weights, and passes the result through a threshold activation function to make a binary decision (output 0 or 1). - **Use Cases**: Early image recognition, classification tasks (though it is limited for complex data). - **Activation Functions**: - **Description**: Activation functions introduce non-linearity into the network, allowing it to model complex data patterns. They are applied to the output of each neuron to decide whether it should fire. - **Common Types**: - **Sigmoid**: Maps input values to a range between 0 and 1. - **Tanh**: Maps input values to a range between -1 and 1. - **ReLU (Rectified Linear Unit)**: Outputs zero for negative inputs and the input itself for positive values, allowing for faster training. - **Use Cases**: Each activation function has different properties and is used based on the task and neural network architecture (e.g., ReLU is often used in deep learning). - **Feedforward Networks**: - **Description**: A feedforward neural network is the simplest type of artificial neural network where connections between the nodes do not form a cycle. Information moves in one direction—from input nodes, through hidden nodes (if any), to the output nodes. - **How It Works**: Data passes through layers of neurons, with weights and activation functions applied at each layer. The final output layer produces the prediction. - **Use Cases**: Used for various tasks such as image classification, pattern recognition, and basic predictive modeling. #### **2. Training Neural Networks** - **Backpropagation**: - **Description**: Backpropagation is the process of adjusting the weights of the neurons in a network to minimize the error in the predictions. It is a key method for training neural networks. - **How It Works**: The error (loss) from the output is propagated back through the network, calculating the gradient of the loss with respect to each weight. The weights are then updated using gradient descent. - **Use Cases**: Backpropagation is used to train feedforward networks, convolutional networks, and recurrent networks. - **Gradient Descent**: - **Description**: Gradient descent is an optimization algorithm used to minimize the loss function by iteratively moving towards the direction of steepest descent. - **Variants**: - **Batch Gradient Descent**: Uses the entire dataset to compute the gradient at each step. - **Stochastic Gradient Descent (SGD)**: Uses a single data point to compute the gradient, making it faster but noisier. - **Mini-batch Gradient Descent**: Combines the benefits of both batch and stochastic gradient descent by using small batches of data. - **Use Cases**: Commonly used for training deep learning models in combination with backpropagation. - **Loss Functions**: - **Description**: A loss function measures how well a neural network’s predictions match the actual output (ground truth). The objective during training is to minimize this loss. - **Common Loss Functions**: - **Mean Squared Error (MSE)**: For regression tasks. - **Cross-Entropy Loss**: For classification tasks. - **Use Cases**: Loss functions are selected based on the nature of the task (regression or classification). #### **3. Deep Learning** - **Convolutional Neural Networks (CNNs)**: - **Description**: CNNs are a specialized type of neural network designed to process data with a grid-like topology, such as images. They use convolutional layers to automatically extract features from the data. - **How It Works**: CNNs consist of convolutional layers, pooling layers, and fully connected layers. The convolutional layers apply filters to the input to extract features such as edges, textures, and shapes. Pooling layers reduce the spatial dimensions of the data, and the fully connected layers output the final prediction. - **Use Cases**: Image classification, object detection, facial recognition, video analysis, and medical image analysis. - **Recurrent Neural Networks (RNNs)**: - **Description**: RNNs are designed for sequential data, where the output at each step depends on previous computations. They have loops in their architecture, allowing them to store information from earlier steps. - **How It Works**: RNNs use the hidden state to remember information across steps in the sequence. Variants like Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRU) improve on the basic RNN by handling long-term dependencies more effectively. - **Use Cases**: Natural language processing (NLP), speech recognition, time series forecasting, and language translation.