How Important Is Deep Learning Modules in Data Science?
Deep Learning is a term that is a part of machine learning methods belonging to the field of artificial intelligence. Earlier in machine learning computers worked with supervised data. Programmers fed the computers with data and complex rules to classify it to recognize speech or objects. Deep learning involves working with unsupervised data. Scientists don’t feed the computer any rules to classify or treat data. The computer thinks on its own based on algorithms of the deep learning scientist. It gains experience by using multi-layers in complex structures similar to our brain neurons.
According to Data scientists Deep learning mimics the neural network in the human brain. Human brains work with the help of neurons that communicate with each other. Similarly, scientists, transform computers into intelligent brains to improve speech recognition, image recognition, natural language processing and more. Deep learning loosely mimics activity in multiple layers of neurons in human brains. As we learn these densely interconnected neurons exchange signal with other cells creating new connections. Similarly, deep learning evolves from non-linear, multi-layered processing of data that enables computers to sophisticated concepts from basics. It is a rapidly growing area of machine learning with the help of knowledge of the human brain, statistics and applied math over past decades. It focuses on algorithms to extract higher levels of abstractions in data by using Artificial Neural Networks with multiple hidden layers.
Deep learning is vital for data science. In the modern world, data is becoming more vital and accessible that makes companies invest heavily in deep learning. Companies understand the importance of deep learning for future technologies of computational science and are sharing their discoveries and conclusions with public. Researchers have always understood the importance of deep learning however it has become more used in the present.
Deep Learning trains a computer to solve intuitive problems by systematically navigating through multiple layers of information through hierarchical progression.
Big companies like Google and Facebook are investing money, resources in research technologies.Companies like Apple, Baidu, Enlitic, Facebook, Google, IBM, Microsoft, Nervana Systems, PayPal, Pinterest, Skymind, Twitter, United Technologies, Yahoo are using Deep learning technologies.
Applications of Deep Learning
- Natural Language processing– This helps the computer to understated what you are saying; this is useful for translation, discourse analysis, and text summary.
- Image recognition – This helps in searching, sorting and segmenting images for object detection. This is highly useful for scientific applications, e-commerce and social networking and advertising.
- Speech Recognition– Speech recognition is used when you tell your smart-phone to type text, or dial a number or tell your car to select a radio station.
Neural networks are not only for large companies. Deep learning technology is applied to all fields and companies of all sizes, from healthcare to automotive, from aerospace to finance industry.
Deep Learning is considered as one of the biggest strategic trends and is set to be one of the top trends in the future. It is fast becoming an important technology that is and will be used across multiple industries.
