Neural networks, also known as artificial neural networks, are an essential subfield of artificial intelligence and machine learning that takes inspiration from the structure and functioning of the human brain.

They are powerful models that can learn complex patterns and relations present within data and automatically extract relevant information from raw data, making them excellent at handling vast unstructured data.

Neural networks consist of interconnected ‘artificial nodes’ divided into ‘layers’.

There are three main types of layers:

• The input layer receives data or features
• The hidden layer performs computation and transformation
• The output layer produces desired predictions

Each node in a neural network receives an input, applies mathematical operations on it, and produces an output. Operations here involve multiplying the input data by ‘weights’ (parameters representing the strength of a connection between nodes) and then summing the results.

When training a neural network with labelled data, weights between nodes are adjusted using ‘backpropagation’, an optimizing algorithm.

Neural networks are of various types, each designed for specific tasks:

Feedforward Neural Networks:

• It is a simple neural network where information flows unidirectionally without loops or feedback connections.

Convolutional Neural Networks (CNNs):

• CNNs have unique layers to automatically detect patterns, such as edges or textures in the input data, making them useful for image and video processing.

Recurrent Neural Networks (RNNs):

• RNNs have connections that allow information to flow in cycles. They can loop and provide feedback to capture temporal dependencies.
• Temporal dependencies refer to relationships between events where the occurrence of one event depends on previous or even future events.
• RNNs are useful for sequential data like time series or natural language processing.

Long Short-Term Memory (LTSMs):

• It is a type of RNN which addresses the ‘vanishing gradient’ problem occurring when deep networks train over long sequences by introducing a focused memory cell.
• ‘Vanishing gradient’ refers to an issue occurring when gradients, the rate of change of function parameters, become minuscule and approach zero as they backpropagate through multiple layers, thus hindering learning.
• It resulted in deep learning networks being unable to learn complex patterns and capture long-term dependencies in large data sequences.
• LTSMs captured long-term dependencies by selectively capturing, retaining and updating information to a focused memory cell.

Generative Adversarial Network:

• GANs can generate new data samples that mimic the characteristics of their training data.
• They consist of two main components, a generator network, which generates synthetic data samples & a discriminator network which tries to distinguish between original and fabricated data.
• These networks train simultaneously, where the generator attempts to produce realistic data to fool the discriminator, and the discriminator tries to distinguish between original and fabricated data.
• Image synthesis and data augmentation use GANs to operate.

Neural networks, with increased computational power, large datasets, and advanced optimizing algorithms, have made remarkable advancements contributing to numerous breakthroughs in many domains that display the untapped potential of artificial intelligence systems and what they can achieve in the future.