Deep Learning is a new area of Machine Learning research, which has been introduced with the objective of moving Machine Learning closer to one of its original goals: Artificial Intelligence. Computer Vision Deep learning models are trained on a set of images training data, to solve a task. These deep learning models are mainly used in the field of Computer Vision which allows a computer to see and visualize like a human would. Deep Learning is a subfield of machine learning concerned with algorithms inspired by the structure and function of the brain called artificial neural networks.
Difference between deep learning, machine learning and artificial intelligence
Artificial Intelligence is a way of making a computer, a computer-controlled robot, or a software think intelligently, in a similar manner the intelligent humans think.AI encompasses many fields of research, from genetic algorithms to expert systems, and provides scope for arguments over what constitutes AI.
Machine learning is an application of artificial intelligence (AI) that provides systems with the ability to automatically learn and improve from experience without being explicitly programmed. Machine learning focuses on the development of computer programs that can access data and use it learn for themselves or computer learns by example from labelled data, and unsupervised learning, where the computer groups similar data and pinpoints anomalies.
Deep learning is a subset of machine learning, whose capabilities differ in several key respects from traditional shallow machine learning, allowing computers to solve a host of complex problems that couldn’t otherwise be tackled. Deep learning models can be visualized as a set of points each of which makes a decision based on the inputs to the node. This sort of network is similar to the biological nervous system, with each node acting as a neuron within a larger network. Thus, deep learning models are a class of artificial neural networks.
HOW IS DEEP LEARNING BEING USED ?
For recognizing and generating images, speech and language, and in combination with reinforcement learning to match human-level performance in games. Deep-learning systems are a foundation of modern online services. Amazon used deep learning to understand what you say — both your speech and the language you use — to the Alexa virtual assistant or by Google to translate text on google translate
Every Google search uses multiple machine-learning systems, to understand the language in your query through to personalizing your results, so fishing enthusiasts searching for “bass” aren’t inundated with results about guitars.
But beyond these very visible manifestations of the machine and deep learning, such systems are starting to find a use in just about every industry. These uses include: computer vision for driverless cars, drones and delivery robots; speech and language recognition and synthesis for chatbots and service robots; facial recognition; helping radiologists to pick out tumours in x-rays, aiding researchers in spotting genetic sequences related to diseases and identifying molecules that could lead to more effective drugs in healthcare; allowing for predictive maintenance on infrastructure by analyzing IoT sensor data; underpinning the computer vision that makes the cashier-less Amazon Go supermarket possible, offering reasonably accurate transcription and translation of speech for business meetings the list goes on and on.
INFOLKS IN DEEP LEARNING
We annotate billions of images and having high-quality work efficiency in image annotation for about 3 years experience .we gave non stop labelling solution built with the human-in-the-loop machine learning platform doing all sort of annotation works like Bounding Box Annotation, Tagging, Point & Dot Annotation, Cuboidal annotation, Polygonal Annotation and Line & Splines Annotation