Introduction to Neural Networks and Their Structures
Neural networks, or Artificial Neural Networks (ANN) to be more specific, are a collection of nodes aligned in a series of layers and used to help train machine and deep learning algorithms. They learn to spot patterns that help them make more accurate decisions with a network of nodes, similar to neurons and inspired by the human brain.
ANNs are a foundational tool in modern AI, aiding the development of machine and deep learning. Their impact can be seen everywhere from spam filters to more intuitive programs like ChatGPT and Google’s search. So, what are neural networks and how do they work?
Understanding Neural Networks
Composed of a large number of interconnected nodes that process input data before passing data onto the next layer of nodes. Each node is assigned a specific computation that data must pass through with each layer of nodes containing increasingly more complex functions.
The machine continues this training process and eventually adjusts weights and biases on its own to match the desired output. This cycle is repeated until the network's output closely matches the desired output.
Key Components of Neural Networks:
ANNs are made up of several key pieces that all work together to help the machine train itself to make more accurate predictions:
Nodes: Processing units within a neural network. Each node receives an input, transforms it via computation, and produces an output for the next layer of nodes.
Layers: Neural networks are structured by layers. Each layer contains a set of nodes. There are typically three types of layers: the input layer, hidden (middle) layers, and the output layer.
Weights: Parameters associated with each connection between nodes. During training, the machine adjusts these weights based on the error of its predictions, which helps it improve its accuracy over time.
Bias: An additional parameter for nodes to make adjustments on outputs independent of the input data.
Activation Functions: A part of the computation that occurs within each node, allowing for non-linearity which helps spot patterns.
Types of Neural Networks
There are many different varieties of neural networks within machine and deep learning that all range in use and application:
Feed-forward Neural Networks (FFNNs): FFNNs feed data in one direction only (forward) without any looping, meaning that data is only processed once by the network.
Recurrent Neural Networks (RNNs): A network that cycles data inputs multiple times through the hidden layers to form a memory.
Convolutional Neural Networks (CNNs): A specially designed network that processes grid-like data such as images and video.
Structure of Neural Networks
ANN layers work by having the input layer first receive raw input data. This is then transferred by the nodes to the hidden layers where data begins processing until it is sent to the output layer. Then, depending on the network (feed-forward or recurrent), the data will either stop or repeat the cycle.
One of the key differences between machine and deep learning is the number of layers within a model. A network isn't considered 'deep' unless it contains multiple hidden layers, but there's no standard threshold. Three layers, inducing the input and output, are considered the bare minimum for a deep learning model.
Input Layer: The first layer of a network, receiving raw input data.
Hidden Layers: Layer between the input and output where most of the computations are performed.
Output Layer: The final layer in a network that produces the end result.
Depth: The number of layers in a neural network.
Width: The number of nodes in a layer.
Training Neural Networks
Training a neural network contains two primary phases: forward propagation and backpropagation. Both processes are repeated based on the number of training cycles programmed into the model.
Forward propagation begins when the input layer first receives data. The model begins to process the information through layers of nodes making sure to factor in weights, bias, and activation functions.
The second phase, backpropagation, occurs after a network has made its initial predictions, comparing it to the final output. The network will then measure the differences between the two, applying a loss function to calculate for error before passing data back to the input layer.
Training would not be possible without the loss function which quantifies the distances between prediction and result. This allows the model to measure its own performance, reinforcing accurate estimations for the next training cycle.
Applications of Neural Networks
Neural networks are becoming more frequent as technology continues to advance. They are often found in Image Recognition software and used for natural language processing, providing us with more intuitive computer programs for smartphones and computers.
Sectors like the healthcare industry rely heavily on networks like CNNs because of their ability to aid doctors in recognizing cancerous tumors or other abnormalities through MRI scans. With an ability to spot the most subtle patterns, these networks can potentially detect malignancies well before a human.
