Atencio M, Jess M and Dewar K (2012) `It is a case of changing your thought processes, the way you actually teach': Implementing a complex professional learning agenda in Scottish physical education. Physical E
Gunawan et al. propose an Extensible Immunofluorescence (ExIF) strategy that integrates distinct 4-plex image panels from routine fluorescence microscopy into multiplexed datasets enabling quantitative interrogation of complex, multimolecular single-cell processes. ...
Unsupervised machine learning employs a more independent approach, in which a computer learns to identify complex processes and patterns without relying on previously labeled data. Unsupervised machine learning not only involves training based on data that doesn’t have labels; there’s also no specific...
For this reason, deep learning is rapidly transforming many industries, including healthcare, energy, finance, and transportation. These industries are now rethinking traditional business processes. Some of the most common applications for deep learning are described in the following paragraphs. In Azure...
in which a computer learns to identify complex processes and patterns without relying on previously labeled data. Unsupervised machine learning not only involves training based on data that doesn’t have labels; there’s also no specific, defined output, such as whether an email is likely spam. ...
The report helps in automation by enabling the export of login and access data to the FTP, where it can be joined with other reports to create comprehensive dashboards. This feature is particularly useful for organizations that rely on automated processes for data analysis and reporting. ...
Deep learning is a subset of machine learning that uses multilayered neural networks, to simulate the complex decision-making power of the human brain.
During animal development, embryos undergo complex morphological changes over time. Differences in developmental tempo between species are emerging as principal drivers of evolutionary novelty, but accurate description of these processes is very challenging. To address this challenge, we present here an aut...
is the source of these two kinds of information. To address this, we propose the Federated Conditional Policy (FedCP) method, which generates a conditional policy for each sample to separate the global information and personalized information in its features and then processes them by a global ...
that isn't a dog." As the toddler continues to point to objects, they become more aware of the features that all dogs possess. What the toddler is doing, without knowing it, is clarifying a complex abstraction: the concept of a dog. They're doing this by building a hierarchy in which...