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VMS
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A Quick History Of Boolean Algebra
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Jamf After Dark: Going Technical on Identity
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The History of The Punch Card
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Sentiment Analysis In Action
Sentiment analysis is the automated analysis of content like text or speech as positive, neutral or negative. That positive or negative analysis can be considered looking at the polarity of the words and a result of sentiment analysis is often a polarity score. This type of scoring allows organizations to better understand how constituents see them. This might mean analyzing Twitter or Facebook, tagging a sentiment score on blog posts or comments on posts. Using technology like sentiment analysis we get insight into how clients and employees see products, the company, support cases, or services. This is usually done using natural language processing part of a body of work we…
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The History of Computing: The Y2k “Bug”
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New Perspectives On The Three Horizons Model
For a long time, the McKinsey Three Horizons model has been a systematic approach to look at the strategy of innovation in larger companies. It laid out three horizons, or time lines, that innovation comes in. Many enterprises have used the model to stay competitive in emerging markets. But we do so with a lower impact today than we used to. Even though incumbents in markets continue to grow quickly. Until we stop. Defining The Three Horizons Model The model came out in 2000, the same year the dot com bubble burst. Think about this: when Baghai, Coley, and White wrote The Alchemy of Growth, it was a disruptive model.…
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Generic Machine Learning Recommender Script
Been working more on building really generic and simple machine learning tools. This one is a generic recommendation script built to run as a lambda or gcf. This iteration on my GitHub is built to run locally but it’s straight forward enough to import json, parse, and run it as a microservice. Requirements numpy gensim nltk==3.4.5 textblob==0.15.3 Usage Run locally, the recommender crawls through a column of a csv and matches the recommendations for similar content. Those are based on the content passed in the –text field. Can use the –recs option to define the number of recs you’d like to recieve in response. python recommender.py --file='my.csv' --text="Flash update" --column="title"…
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MacAdmins Podcast: Arek Dreyer on macOS Support Essentials