Neural Networks
Overview / Background
Neural networks are at the heart of deep learning. These are architectures which mimic the design of the brain and have the capability to be trained to tackle any problem.
Underlying a neural network are neurons, layers, weights and biases.
Key Concepts
- Neurons: Basic units of a neural network, which process inputs and generate an output.
- Layers: Neural networks consist of input layers, hidden layers, and output layers.
- Weights and Biases: Parameters within the network that are adjusted during training to minimize error.
- Activation functions: Functions which allow non-linearity.
Main
Neural networks are composed of layers of nodes. Each node is a perceptron that performs a weighted sum of its inputs and passes the result through an activation function.
Steps in a Neural Network
- Input Layer: Receives the input data.
- Hidden Layers: Perform computations and transfer information from the input layer to the output layer.
- Output Layer: Provides the final output.
Backpropagation
A method used to calculate the gradient of the loss function and update weights in the network to minimize error.
Examples
Deep Neural Networks (DNN)
Deep Neural Networks (DNN) are neural networks with multiple hidden layers between the input and output layers. They are capable of learning complex patterns in data by transforming the data through several layers of non-linear operations.
Convolutional Neural Networks (CNN)
Convolutional Neural Networks (CNN) are specialised neural networks and can be applied on images. A CNN consists of a kernal and filter.
Long Short-Term Memory (LSTM) Networks
Long Short-Term Memory (LSTM) networks are a type of recurrent neural network (RNN) that are capable of learning long-term dependencies. They are well-suited for sequence prediction problems where the context is important, such as time series forecasting and natural language processing.
Gated Recurrent Unit (GRU) Networks
Gated Recurrent Unit (GRU) networks are a variation of LSTM networks that use gating units to modulate the flow of information. GRUs are simpler and computationally more efficient than LSTMs while still being effective for capturing dependencies in sequential data.
Conclusion
Neural networks are powerful tools for various applications in machine learning and artificial intelligence. With the right understanding and tools, you can build models that solve complex problems and provide valuable insights.