Category: Machine Learning
Machine Learning – Introduction to Quick and Accurate Machine Translation
Machine translation model renders text from one natural language to another. There are various MT models developed to effectively drive translation based application souls. To accomplish this task, experts rely on the powerful approaches used to build these models..
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Demystifying Deep Learning – Back to Basics
Deep Learning (DL) employs multiple layers of non-linear training units in order to facilitate feature extraction and transformation. The phenomenon of artificial intelligence has induced significant transformation in contemporary enterprises due to its demonstration of a degree of accuracy in specific endeavors that is akin to human capabilities. A variety of tasks, including but not limited to design acknowledgment, pattern recognition, picture classification, and voice/text interpreting, are frequently employed in the respective field under consideration. Self-driving CAR is one of the best examples and biggest achievements so far.
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Uncovering Anxious Deep Learning for Ease
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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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.
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The Exciting Evolution of Machine Learning
Machine learning is the process of a machine attempting to accomplish a task, independent of human intervention, more efficiently and more effectively with every passing attempt i.e learning phase. At this point, AI- a machine which mimics the human mind, is still a pipe dream. In the middle we have the meat of the pipeline, the model, which is the machine learning algorithm that learns to predict given input data.
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How to Friends with Statistics and MachineLearning
While there may be variances in methodology between machine learning and statistics, these distinctions do not sever the alliance between the two fields. The contrast lies in the fact that machine learning prioritizes enhancing efficiency and achieving optimal results, while the others focus on sample size, population, and developing hypotheses. The primary focus of machine learning is on producing accurate forecasts, even if the explanations behind those predictions are not easily comprehensible.
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Can Cybersecurity be Entrusted with AI?
While the technology can help to fill cybersecurity skill gaps but at the same time it’s a powerful tool for hackers as well. In short, AI can act as a guard and threat at the same time. What matter is who uses it for what purpose. In the end, it all depends upon Natural Intelligence to make good or bad use of Artificial Intelligence.
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FinTech – Machine Learning and Recommenders
These terms are used to from BI Intelligence, To illustrate the various applications of AI in eCommerce and use case studies to show how this technology has benefited merchants/ecommerce service providers. Different consumers have varying, and often very specific, requirements for product, needs, expect performance, cost of consumption, silicon wafer thin kind of cost for best thing in mind and other parameters.
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Robots and Artificial Intelligence
As per professor Andrew machine learning is a revolution in history of man kind first one was where machines came and took our basic jobs to make us faster and our life easy. Now to me this second one is coming to make us lazy and heavily dependent. The technique that made easy work easy possibly is no longer AI, but something mundane.
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Machine Learning as a Service – MLaaS
Whoever has the best sense for choosing, organizing, uniqueness to combine machine and human skills outlook from the services to collect, clean and label data sets its a market for them that’s just getting started and millions yes billions of dollars are waiting
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Harnessing Machine Learning in Payments
ML gets the problem-solving call in conjunction with deep learning artificial neural networks. As these jargons i.e AI, ML, DL or ANN etc may be getting their day in the sun, but they’ve been around for a while. It’s just in the past 5-10 years that they have gained traction, technology that was once niche is now becoming more mainstream and cost-effective reaching to common man. Until recent machine learning was known as historical phenomena in the worlds of academia and supercomputing.
