Machine Learning (Deep Learning)

Deep Learning is an artificial intelligence function that imitates the workings of the human brain in processing data and creating patterns for use in decision making. Deep learning is a subset of machine learning in Artificial Intelligence (AI) that has networks capable of learning unsupervised from data that is unstructured or unlabeled. Deep learning (also known as deep structured learning or hierarchical learning) is part of a broader family of machine learning methods based on learning data representations, as opposed to task-specific algorithms. Learning can be supervised, semi-supervised or unsupervised. Deep learning: Deep learning architectures such as deep neural networks, deep belief networks and recurrent neural networks have been applied to fields including computer vision, speech recognition, natural language processing, audio recognition, social network filtering, machine translation, bioinformatics, drug design, medical image analysis, material inspection and board game programs, where they have produced results comparable to and in some cases superior to human experts. Deep learning models are vaguely inspired by information processing and communication patterns in biological nervous systems yet have various differences from the structural and functional properties of biological brains (especially human brains), which make them incompatible with neuroscience evidences. Machine learning: Machine learning is an application of artificial intelligence (AI) that provides systems 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. The process of learning begins with observations or data, such as examples, direct experience, or instruction, in order to look for patterns in data and make better decisions in the future based on the examples that we provide. The primary aim is to allow the computers learn automatically without human intervention or assistance and adjust actions accordingly. Differences between deep learning and machine learning algorithms i. Machine learning uses types of automated algorithms which learn to predict future decisions and model functions and model functions using the data fed to it while deep learning interprets data features and its relationships using neural networks which pass the relevant information through several stages of data processing. ii. The Output in machine learning is usually a numerical value, like a score or a classification while in deep learning, the output can be anything from a score, an element, free text or even sound. iii. In machine learning, usually there are a few thousand data points used for the analysis while in deep learning, there are a few million data points used for the analysis. iv. Machine learning has various algorithms that are directed by the analysts to examine the different variables in the datasets while deep learning, once they are implemented, the algorithms are usually self-directed for the relevant data analysis.