Learn about recent research that is the first to explain a surprising phenomenon where in BERT/Transformer-like architectures, deepening the network does not seem to be better than widening (or, increasing the representation dimension). This empirical observation is in contrast to a fundamental premise in deep learning.
Data Mechanics is developing a free monitoring UI tool for Apache Spark to replace the Spark UI with a better UX, new metrics, and automated performance recommendations. Preview these high-level feedback features, and consider trying it out to support its first release.
Data privacy laws, such as the CCPA, GDPR, and HIPAA, are here to stay and significantly impact everyone in the digital era. These steps will guide organizations to prepare for compliance and ensure they support the fundamental privacy rights of their customers and users.
While hundreds of machine learning tools are available today, the ML software landscape may still be underdeveloped with more room to mature. This review considers the state of ML tools, existing challenges, and which frameworks are addressing the future of machine learning software.
Most massive open online courses are too superficial because they offer introductory-level courses. For in-depth knowledge, more is needed to increase your knowledge and expertise after establishing a foundation.
Since that renowned conference at Dartmouth College in 1956, AI research has experienced many crests and troughs of progress through the years. From the many lessons learned during this time, some have needed to be re-learned -- repeatedly -- and the most important of which has also been the most difficult to accept by many researchers.
Get a handle on how deep learning is affecting the finance industry, and identify resources to further this understanding and increase your knowledge of the various aspects.
The major advantage of focusing on AI-based methods is that they tackle each of the challenges faced by farmers from seed sowing to harvesting of crops separately and rather than generalising, provide customised solutions to a specific problem.
The results show that despite the deluge of Big Data, large majority still works in Gigabyte or Megabyte-size datasets. Data Scientists work with the largest-size datasets, followed by Data Engineers, Data Analysts, and Business Analysts. Read more for details.
Data Science MOOCs are too Superficial
The Bitter Lesson of Machine Learning
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