Neural Networks
Neural networks are a class of machine learning models inspired by the human brain. They consist of interconnected nodes (neurons) organized in layers that can learn complex patterns from data.
What are Neural Networks?
Neural networks are computational models inspired by the structure and function of the human brain. They consist of interconnected processing nodes (neurons) organized in layers that work together to learn patterns from data. Each connection between neurons has a weight that adjusts during learning.
Neural networks can learn to perform tasks by considering examples, generally without being programmed with task-specific rules. They excel at finding patterns in complex, high-dimensional data and can be used for both classification and regression tasks.
Key Characteristics
- Composed of layers of interconnected neurons
- Learn through a process called backpropagation
- Can approximate any continuous function (universal approximation theorem)
- Require large amounts of data and computational resources
- Can handle complex, non-linear relationships in data
- Different architectures specialized for different data types (images, text, etc.)
Neural Network Architectures
A class of feedforward artificial neural network that consists of at least three layers of nodes: an input layer, a hidden layer and an output layer.
Deep learning architecture specifically designed for processing grid-like data such as images, using convolutional layers.
Neural networks designed to recognize patterns in sequences of data, such as text, time series, or speech.
A deep learning model that adopts the mechanism of self-attention, differentially weighting the significance of each part of the input data.
Key Components
The basic computational units that receive inputs, apply weights and biases, and pass the result through an activation function to produce an output.
Mathematical functions that determine the output of a neuron. Common examples include ReLU, Sigmoid, and Tanh.
Adjustable parameters that are learned during training. Weights determine the strength of connections between neurons, while biases allow shifting the activation function.
Functions that measure the difference between predicted and actual outputs, guiding the learning process by quantifying how well the model is performing.
Common Applications
Image classification, object detection, facial recognition, and image generation using CNNs and GANs.
Text classification, sentiment analysis, machine translation, and text generation using RNNs and Transformers.
Stock price prediction, weather forecasting, and anomaly detection in sensor data using RNNs and LSTMs.
Training Neural Networks
Training neural networks involves several key concepts and techniques:
- Backpropagation: The algorithm used to calculate gradients of the loss function with respect to the weights, propagating from output to input layers.
- Gradient Descent: An optimization algorithm that iteratively adjusts weights to minimize the loss function.
- Learning Rate: A hyperparameter that controls how much to change the model in response to the estimated error each time the weights are updated.
- Batch Size: The number of training examples used in one iteration of model training.
- Epochs: The number of complete passes through the entire training dataset.
- Regularization: Techniques like dropout and weight decay used to prevent overfitting.