theclevermachine.wordpress.comの競合サイトトップ10

statsblogs.com

Ashley Steel writes: Walking away from science or walking away with science? This is an essay about career transitions and the value of statistical thinking in, perhaps, surprising places. It is written in hopes of opening a conversation. When my father, a kind and distinguished academic physician, gave me a chemistry set for my 12th …Read more →

cs.uu.nl

This is the website of the Utrecht University ICS department.

stat.duke.edu

We are currently updating all of our alumni pages. Please stay tuned.The Department of Statistical Science is extremely proud of its highly accomplished Ph.D., M.S., undergraduate, and postdoctoral alumni. We've included a comprehensive list of all our graduates - including year of graduation, thesis (if available), advisor, and last known employment.

stats.stackexchange.com

Q&A for people interested in statistics, machine learning, data analysis, data mining, and data visualization

r-bloggers.com

sentiment.aiMore powerful sentiment analysis using deep learning models made simple..See github.io page herehttps://benwiseman.github.io/sentiment.ai/OverviewKorn Ferry Institute's AITMI team made sentiment.ai for researchers and tinkerers who want a straight-forward way to use powerful, open source deep learning models to improve their sentiment analyses. Our approach is relatively simple and out performs the current best offerings on CRAN and even Microsoft's Azure Cognitive Services. Given that we felt the current norm for sentiment analysis isn't quite good enough, we decided to open-source our simplified interface to turn Universal Sentence Encoder embedding vectors into sentiment scores.We've wrapped a lot of the underlying hassle up to make the process as simple as possible. In addition to just being cool, this approach solves several problems with traditional sentiment analysis, namely:More robust, can handle spelling mitsakes and mixed case, and can be applied to dieciséis (16) languages!Doesn't need a ridged lexicon, rather it matches to an embedding vector (reduces language to a vector of numbers that capture the information, kind of like a PCA). This means you can get scores for words that are not in the lexicon but are similar to existing words!Choose the context for what negative and positive mean using the sentiment_match() function. For example, you could set positive to mean "high quality" and negative to mean "low quality" when looking at product reviews.Power Because it draws from language embedding models trained on billions of texts, news articles, and wikipedia entries, it is able to detect things such as "I learned so much on my trip to Hiroshima museum last year!" is associated with something positive and that "What happeded to the people of Hiroshima in 1945" is associated with something negative.The power is yours We've designed sentiment.ai such that the community can contribute sentiment models via github. This way, it's easier for the co

mathworks.com

Contact MathWorks Support or browse support resources

akita-pu.ac.jp

秋田県立大学 開学20周年記念誌

mrc-cbu.cam.ac.uk

using cognitive theory and innovations in neuroscience to understand and improve mental wellbeing across the lifespan