Month: April 2018
Why GDPR will Make Machine Learning not so legal
Does GDPR require Machine Learning algorithms to explain their output? may be yes may be no or in short Probably not, but there is enough ambiguity to be clarified and keep DataScientists, Lawyers, industry influencers busy.
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Astonishing Hierarchy of Machine Learning Needs
Present-day technology has allowed machines to achieve skills previously exclusive to humans, such as improved decision-making abilities and game proficiency. The ability for machines to analyze and recognize patterns and subsequently retain this information for future reference has made it achievable. Nowadays, the biggest challenge lies in locating capable resources who can display and distinguish their knowledge acquired from practical business scenarios rather than indulging in online debates with others, as found in university and PhD literature.
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Reinforcement Learning – Reward for Learning
Reinforcement learning can be understood by using the concepts of agents, environments, states, actions and rewards. This is an area of machine learning; where there’s no answer key, but RL agent still has to decide how to act to perform its task. The agent is inspired by behaviourist psychology who decide how and what actions will taken in an environment to maximize some notion of cumulative reward.
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Unsupervised Learning an Angle for Unlabelled Data World
System does self-discovery of patterns, regularities and features etc. from the input data and relations for the input data over output data. Discovering similarities and dissimilarities to forms clusters i.e. self-discovery is main target here. Since the examples given to the learner are unlabeled
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Supervised Machine Learning – Insider Scoop for labelled data
Machine learning algorithms “learns” from the observations. When exposed to more observations, the algorithm improves its predictive performance. What’s going to happen to the stock market tomorrow? Is a task of deducing function from labeled training data.
