Machine Learning
Machine learning is the science of getting computers to act without being explicitly programmed. The ability to learn of Machine learning is actively being used today, perhaps in many more places than one would expect, starting from vehicles to industries to spacecraft.
Deep Learning
Deep Learning (DL) has become more than just a buzzword in the Artificial Intelligence (AI) community – it is reshaping
global business through the prolific use of autonomous, self-teaching systems, which can build models by directly
studying images, text, audio, or video data. Deep learning uses neural networks with many intermediate layers of artificial “neurons” between the input and the output, inspired by the human brain.
The technology excels at modeling extremely complicated relationships between these layers to classify and predict things.
Deep Learning algorithms try to learn high-level features from data in an incremental manner.
This eliminates the need of domain expertise and hard core feature extraction.
Deep Learning out perform other techniques if the data size is large it really shines when it comes to complex problems
such as image classification, natural language processing, and speech recognition.
Deep Learning (DL) has become more than just a buzzword in the Artificial Intelligence (AI) community – it is reshaping global business through the prolific use of autonomous, self-teaching systems, which can build models by directly studying images, text, audio, or video data. Deep learning uses neural networks with many intermediate layers of artificial “neurons” between the input and the output, inspired by the human brain. The technology excels at modeling extremely complicated relationships between these layers to classify and predict things.
Deep Learning algorithms try to learn high-level features from data in an incremental manner. This eliminates the need of domain expertise and hard core feature extraction. Deep Learning out perform other techniques if the data size is large it really shines when it comes to complex problems such as image classification, natural language processing, and speech recognition.
Natural Language Processing (NLP)
Natural Language Processing (NLP) is a sub-field of Artificial Intelligence that is focused on enabling computers to understand and process human languages, to get computers closer to a human-level understanding of language. Recent advances in Machine Learning (ML) have enabled computers to do quite a lot of useful things with natural language. Deep Learning has enabled us to write programs to perform things like language translation, semantic understanding, and text summarization.
NLP will become an important technology in bridging the gap between human communication and digital data. With the ongoing growth of the World Wide Web and social media, there is a drastic increase in online data. As the amount of data increases it will be very difficult to find a specific piece of information from a large unstructured knowledge base. These challenges and difficulties can be overcome with the advanced NLP techniques.
Future application of NLP will be more user-oriented, like NLP will be able to understand the user’s real intent ,NLP will be programmed to understand more complex elements of the human language such as humour, sarcasm, satire, irony and cynicism. with the advancement of NLP, the differences between natural language and machine language will be blurred.
Computer Vision
Computer vision is concerned with the automatic extraction, analysis and understanding of useful information from imagery(both photos and videos)
by applying applies machine learning and deep learning to recognize patterns for interpretation of images
It involves the development of a theoretical and algorithmic basis to achieve automatic visual understanding.
Using digital images from cameras and videos and deep learning models, machines can accurately identify and classify objects — and then react to what they “see.” Accuracy rates for object identification and classification have gone from 50 percent to 99 percent in less than a decade — and today’s systems are more accurate than humans at quickly detecting and reacting to visual inputs.
Computer Vision allows computers, and thus robots, other computer-controlled vehicles, and everything from factories and farm equipment to semi-autonomous cars and drones, to run more efficiently and intelligently and even safely.
The market for computer vision is developing nearly as fast as the capacities. It’s anticipated to reach $26.2 billion by 2025, developing more than 30% for every year. Artificial intelligence is the future, and computer vision is the most amazing appearance of that future.
Computer vision is concerned with the automatic extraction, analysis and understanding of useful information from imagery(both photos and videos) by applying applies machine learning and deep learning to recognize patterns for interpretation of images It involves the development of a theoretical and algorithmic basis to achieve automatic visual understanding.
Using digital images from cameras and videos and deep learning models, machines can accurately identify and classify objects — and then react to what they “see.” Accuracy rates for object identification and classification have gone from 50 percent to 99 percent in less than a decade — and today’s systems are more accurate than humans at quickly detecting and reacting to visual inputs. Computer Vision allows computers, and thus robots, other computer-controlled vehicles, and everything from factories and farm equipment to semi-autonomous cars and drones, to run more efficiently and intelligently and even safely.
The market for computer vision is developing nearly as fast as the capacities. It’s anticipated to reach $26.2 billion by 2025, developing more than 30% for every year. Artificial intelligence is the future, and computer vision is the most amazing appearance of that future.