NEURAL NETWORK
Neural networks reflect the behavior of the human brain, allowing computer
programs to recognize patterns and solve common problems in the fields of
AI, machine learning, and deep learning.
What are neural networks?
Neural networks, also known as artificial neural networks (ANNs) or
simulated neural networks (SNNs), are a subset of"machine learning"and are
at the heart of"deep learning"algorithms. Their name and structure are
inspired by the human brain, mimicking the way that biological neurons
signal to one another.
Artificial neural networks (ANNs) are comprised of a node layers, containing
an input layer, one or more hidden layers, and an output layer. Each node, or
artificial neuron, connects to another and has an associated weight and
threshold. If the output of any individual node is above the specified
threshold value, that node is activated, sending data to the next layer of the
network. Otherwise, no data is passed along to the next layer of the network.
Neural networks rely on training data to learn and improve their accuracy
over time. However, once these learning algorithms are fine-tuned for
accuracy, they are powerful tools in computer science and"artificial
intelligence, allowing us to classify and cluster data at a high velocity. Tasks
in speech recognition or image recognition can take minutes versus hours
when compared to manual identification by human experts. One of the most
well-known neural networks is Google’s search algorithm.
How does Neural Network work?
Think of each individual node as its own"linear regression"model, composed
of input data, weights, a bias (or threshold), and an output. The formula
would look something like this:
∑wixi + bias = w1x1 + w2x2 + w3x3 + bias
