Deep learning is an advanced machine learning technique that utilizes artificial neural networks in order to simulate the executive processes of the human brain. Deep neural networks consist of multiple layers of interconnected nodes, each designed to build upon the previous layer to refine a prediction or categorization.
This progression of computations through the network is called forward propagation. Another process called backpropagation uses algorithms to calculate errors in predictions and then adjusts the weights and biases of the function by moving backwards through layers to train the model. It is these processes combined that allow the system to learn from vast quantities of information while also being capable of review and flexibility.
Deep learning is the driving force behind many artificial intelligence (AI) applications and services, automating analytical tasks without the need for human intervention.
With applications in financial services to virtual assistants to healthcare and law enforcement, the probability of encountering such a program grows higher every day.
As of 2021, Grand Market Research valued the market at an estimated $8.56 billion. After two years of unprecedented growth the market is now near an estimated value of $49.6 billion. GMRs revenue forecast also predicts that by 2030, deep learning will become an industry worth $526.7 billion dollars.
IDC research indicates a significant rise in the use of AI and machine learning and identifies cost and lack of expertise as some of the top inhibitors preventing their adoption at large. There are also significant hardware requirements to running such programs requiring a tremendous amount of processing power, and with high-powered graphical processing units already in short supply, adoption of this technology is costly at least.