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Has Machine Learning Made Cryptocurrencies Traceable?

Smart Data Collective

Machine learning has become a major game changer for the cryptocurrency industry. Most of the benefits are machine learning have been positive for the market. Machine learning is being used to predict price patterns more easily. Machine learning is making cryptocurrencies easier to trace.

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New DoE Program Drives Demand For Machine Learning Programmers

Smart Data Collective

Machine learning is leading to numerous changes in the energy industry. The Department of Energy recently announced that it is taking steps to accelerate the integration of machine learning technology in energy research and development. Machine learning is already disrupting the global energy industry on a massive scale.

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Adding Common Sense to Machine Learning with TensorFlow Lattice

The Unofficial Google Data Science Blog

On the other hand, sophisticated machine learning models are flexible in their form but not easy to control. Introduction Machine learning models often behave unpredictably, as data scientists would be the first to tell you. A more general approach is to learn a Generalized Additive Model (GAM).

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Financial services firms turn to automated, data-driven processes for new products and services

CIO Business Intelligence

Between the host of regulations introduced in the wake of the 2009 subprime mortgage crisis, the emergence of thousands of fintech startups, and shifting consumer preferences for digital payments banking, financial services companies have had plenty of change to contend with over the past decade.

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Explaining black-box models using attribute importance, PDPs, and LIME

Domino Data Lab

The interest in interpretation of machine learning has been rapidly accelerating in the last decade. This can be attributed to the popularity that machine learning algorithms, and more specifically deep learning, has been gaining in various domains. PDPs for the bicycle count prediction model (Molnar, 2009).

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AI Advances Drive New Generation of Browser-Based Solutions

Smart Data Collective

It’s seemingly compulsory for most developers to build mobile versions of their applications or risk losing millions of potential users. This includes utilizing personalization technology, which relies heavily on machine learning. Many people tend to forget their app updates, which can pose significant risks.

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ML internals: Synthetic Minority Oversampling (SMOTE) Technique

Domino Data Lab

Machine Learning algorithms often need to handle highly-imbalanced datasets. This carries the risk of this modification performing worse than simpler approaches like majority under-sampling. A weighted nearest neighbor algorithm for learning with symbolic features. Machine Learning, 57–78. Chawla et al.