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ATTENTION IS ALL YOU NEED
The dominant sequence transduction models are based on complex recurrent or convolutional neural networks that include an encoder and a decoder. The best performing models also connect the encoder and decoder through an attention mechanism. This paper proposes a new simple network architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two machine translation tasks show these models to be superior in quality while being more parallelizable and requiring significantly less time to train.
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The dominant sequence transduction models are based on complex recurrent or convolutional neural networks that include an encoder and a decoder. The best performing models also connect the encoder and decoder through an attention mechanism. This paper proposes a new simple network architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two machine translation tasks show these models to be superior in quality while being more parallelizable and requiring significantly less time to train.
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REGRESSION IN MACHINE LEARNING
Regression, a statistical approach, dissects the relationship between dependent and independent variables, enabling predictions through various regression models.
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Regression, a statistical approach, dissects the relationship between dependent and independent variables, enabling predictions through various regression models.
The article delves into regression in machine learning, elucidating models, terminologies, types, and practical applications.
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When we get the data, after data cleaning, pre-processing, and wrangling, the first step we do is to feed it to an outstanding model and of course, get output in probabilities. But hold on! How in the hell can we measure the effectiveness of our model. Better the effectiveness, better the performance, and that is exactly what we want. And it is where the Confusion matrix comes into the limelight. Confusion Matrix is a performance measurement for machine learning classification.
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Machine Learning & Credit Risk : A suitable marriage ?
Since the financial crisis, regulators have put an important focus on risk management supervision and expect banks to have transparent, auditable risk measurement frameworks dependent on portfolio characteristics for regulatory, financial or business decision-making purposes. Quantitative modelling techniques are used to get better insights from data, reduce cost and increase overall profitability.
In this disruptive era of Big Data and Artificial Intelligence, banks are considering the adoption of evolving technological capabilities whilst arbitrating between heightened regulatory demands and business
objectives.
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AN EXECUTIVE'S GUIDE TO AI
Staying ahead in the accelerating artificial-intelligence race requires executives to make nimble, informed decisions about where and how to employ AI in their business. One way to prepare to act quickly: know the AI essentials presented in this guide.
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Staying ahead in the accelerating artificial-intelligence race requires executives to make nimble, informed decisions about where and how to employ AI in their business. One way to prepare to act quickly: know the AI essentials presented in this guide.
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DECISION TREE MODELS
Use decision tree models to develop classification systems that predict or classify future observations based on a set of decision rules. If you have data divided into classes that interest you (for example, high- versus low-risk loans, subscribers versus nonsubscribers, voters versus nonvoters, or types of bacteria), you can use your data to build rules that you can use to classify old or new cases with maximum accuracy. For example, you might build a tree that classifies credit risk or purchase intent based on age and other factors.
This approach, sometimes known as rule induction, has several advantages. First, the reasoning process behind the model is clearly evident when browsing the tree. This is in contrast to other black boxmodeling techniques in which the internal logic can be difficult to work out.
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