"Elementary particles are the building blocks of al matter everywhere in the universe.
Their properties are connected with the fundamental forces of nature"
In this document, the Standard Methodology for Analytical Models (SMAM) is described. A short overview of the SMAM phases can be found in Table 1. The most frequent used methodology is the Cross Industrial Standard Processes for Data Mining (CRISP-DM)[1], which has several shortcomings that translate into frequent friction points with the business when practitioners start building analytical models.
A new curated list of medical data for machine learning is available here.
In addition, Stanford is developing a petabyte-scale, cloud-based, multi-institutional, searchable, open repository of diagnostic imaging studies for developing intelligent image analysis systems - called Medical Image Net.
These curated data sets are great for experimenting - please respect data usage restrictions for each data set.
Today data scientists are using new techniques with machine learning to solve complex challenges. In applied data science speed kills and the Tesla V100 accelerator is built for speed. Data scientists always make trade-offs between accuracy and time. Thus, more powerful compute systems are required to crunch and analyze diverse data sets, and train exponentially more complex deep learning models in a practical amount of time.
Google's 2nd generation tensor processor has four 45 teraflops chips in a 180 TFLOPS Tensor Processor Unit (TPU) - available for use in the cloud later this year. Google calls them Cloud TPUs - initially available on the Google Compute Engine.
The Data Science Association has posted a wide variety of TensorFlow tutorial videos here.
See: 