Deep Learning terminology can be quite overwhelming to newcomers.. This blog post covers important aspect of deep learning which can be defined as set of techniques that uses neural networks to simulate human decision-making skills. #AILabPage
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The Appeal of AI for Cyber Security has extremely good reasons like automation of operational tasks, develop & delivering predictive capabilities, mitigating human biases behaviour and derive actionable intelligence. On the down side AI requires high volumes of high quality data to learn.
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Somebody mentioned it to me some time back and I laughed then. The day you decided to come online you actually accepted “No to privacy”. Now its even worse as we can even use radio signals to see through walls so no need to be online. Signals now can be used to monitor a person’s precise movements through a solid wall. What else and how far we will go cant answer now. But all these development for sure has its pros and cons sides.
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Deep learning is a learning scheme that approaches the learning problem by learning the underlying representations; too much of learning. I thats why its also called as representation learning.
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A technique for implementing machine learning. At the same time I also claim It is absolutely wrong to call Deep Learning as Machine Learning (in my personal opinion). The techniques is to achieve a goal not necessarily come out of same goal. #AILabPage
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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.
The post Why GDPR will Make Machine Learning not so legal appeared first on Vinod Sharma’s Blog.
Machine Learning is hottest subject of today’s time, DataScientist is the sexiest job of today but implementing these buzz words in real life business is most important need. The Brief Machine Learning is the hottest subject of today’s time, DataScientist is the sexiest job of today but […]
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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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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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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.
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