The world is going crazy about Artificial Intelligence (AI) where machines can work like human beings. Machine Learning (ML) makes computers far better than humans in the context of data science. Machines are trained to bring the desired output from the large aggregated data like humans be it predictive analysis about defaulters in financial institutions.
ML algorithms are categorized into two levels by of human intervention, i.e., supervised, and semi-supervised or unsupervised. Semi-supervised or unsupervised hold the capacity of learning on their own and are known as Deep Learning (DL). So, DL is the subset of ML.
Deep Learning is a technique for implementing Machine Learning. Deep Learning trains computers to do human-like tasks such as image recognition, speech recognition, 3-D object recognition, and natural language processing (NLP). It is named 'Deep' because it reads information in stacks and goes deep into several layers. Grand View Research published that the global market size of Deep Learning was valued at $272.0 million in 2016.
Machine Learning is the superset of Deep Learning OR you can say Deep Learning is the subset of Machine Learning. Usually, when many people use the term deep learning, they are referring to the Deep Artificial Neural Networks, and somewhat less frequently to Deep Reinforcement Learning.
The market size is expected to be rapidly growing because other industries like healthcare, finance, automotive, and supply chain, etc. are influenced by its swiftness and precision. Leading tech giants like Apple, Amazon, Google, Flipkart, Paypal, and Paytm are already leveraging its benefits. KellyOCG India says that the demand for AI professionals will be 60 percent higher in 2018 in India.
It�s amazing for us to see computers recognizing images like us and how do we enjoy giving commands to Siri. DL includes unsupervised learning through unstructured or unlabelled data. The mechanism works like human brain where neurons are replaced by nodes. The computation takes place in nodes. The number of node layers through which data passes are single-hidden-layer neural networks.
Each neural network is trained with a data set to perform a task. DL is the robust subset of AI since it also does self-learning by aggregating and recombining the information in neural networks. It is scalable to the desired extent and can be feasibly done by adding more neural networks.
These deep neural networks are made faster and smarter through transfer learning. Re-purposing already trained models for another related task is called transfer learning. Hence, transfer learning saves time by eliminating the need to repeat the process.