The use of neural networks and deep learning is becoming increasingly widespread, as they can provide optimized solutions to many problems in AI such as natural language processing, speech recognition, and image recognition. Neural networks are a programming paradigm that allows a system to learn from data and deep learning is a set of techniques used for learning in these neural networks.
What is a Neural Network?
A neural network is a framework loosely modeled on the biological neural networks that form the animal/human brain. The framework or network is designed to enable different sets of machine learning algorithms to process complex data and recognize patterns.
Like most machine learning technologies, neural networks can be trained to classify data. They help to cluster unlabeled data based upon similarities in the example inputs. They can classify data when they have a labeled dataset to train on. Training helps the neural network “learn” without requiring programming, to perform tasks when faced with new data.
What is Deep Learning
Deep learning has stacked neural networks or multilayered networks. Deep learning algorithms use these multiple layers of nonlinear processing units for feature extraction and transformation. For more on deep learning, read our blog.
What are Deep Neural Networks
Single hidden-layer neural networks tend to be more commonplace. The layers are made of nodes which are roughly modeled on the neurons in the human brain. Deep-learning networks have multiple node layers. The data will pass through these multiple layers while in the process of pattern recognition. In a deep learning network, each layer of nodes trains on a distinct set of features based on the previous layer’s output. This allows the nodes to recognize complex features. This ‘feature hierarchy’ allows deep learning neural networks to classify and recognize the vast troves of multi-dimensional data sets. They can recognize patterns and features in the unstructured, unlabeled data that forms much of the data available today.
Advantages of Deep Neural Networks
These deep neural networks can learn and model non-linear and complex relationships between input and output. They can ‘learn’ from the inputs to infer relationships on new/unseen data and make generalizations and predictions.
Applications of Deep Neural Networks
A Recurrent Neural Network (RNN) is an example of a deep neural network. Automatic Speech Recognition is the most successful application of RNN and is widespread now. Less widespread but growing applications of RNN include music composition and handwriting recognition.
Another example of a deep neural network is a Convolutional Neural Network (CNN). CNNs are widely used in image recognition, facial recognition, and natural language processing. The ability to do fast image recognition results in another key application, self-driving cars, which rely on deep neural nets for data on what a pedestrian may look like, for example. CNNs have been known to accurately identify the style of a painting, and amazingly, generate a painting in the “style” of an artist or period.
Going beyond image recognition, AtomNet, a CNN specializing in healthcare applications, was used to predict novel candidate biomolecules for multiple disease targets, most notably treatments for the Ebola virus and multiple sclerosis.
Forecasting models for stock markets and weather now rely on neural networks to process more complex data sets with non-linear relationships. Deep neural nets have the ability to automatically extract hidden features and relationships even in highly volatile data in a way that traditional forecasting models cannot.
Neural networks have helped systems learn faster, but with the addition of deep learning, the resulting deep neural nets are powerful enough to be game changers across industries.