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Mid-day meditation/nap. Especially if I’ve been going hard. I use a free iOS app named Oak and do a 15 minute meditation, that often turns into a nap, usually right after lunch.

And in the interest in trying to see other people. If I don’t have a client meeting scheduled, I’ll often try and meet someone in the industry for a coffee or lunch, assuming my schedule is manageable.

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Rony Ascencio June 29, 2018 at 10:54 pm

I relly like this but I want to know what can we do if we never work remotely because I’m from Mexico and here it’s really hard to work or collaborate with others artist remotely, not a lot of people trust to freelance people to work remotely hope you can help me

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July 2, 2018 at 10:42 am

Very nice guide! I’m working remotely about 10 years, and in this time I’ve changed my workflow that now I’m very much productive than at the start. Sure the tips you’re saying are very nice and helpful. I’ve added some tips to improve my productivity.

First one use define some kind of action are you doing during an ideal week. Then create in google calendar a new voice with its own color for that list you’ve just done. Fill on Sunday the next week with appoinments, work you have to do, etc or even more if you know that already. The best one is to start with your personal things to do like reading, sports, family, etc. Then fill the gaps with the work. Don’t fill every gap! Take some hour free because you really don’t know things are going like you think Then when you work you could use some nice tecniques to improve your work. If you are using only the PC, turn off all the notification from everything that would beep. Don’t use music if you are going to do something creative thinking, that’s better. Use mind maps to make clear and an understandable path you work through. Then use the pomodoro tecnique ( https://francescocirillo.com/pages/pomodoro-technique ) and do 1 thing at a time. If you are doing anything, choose one thing to do and make sure you’re doing only that one! This is important because everything that is distracting you to do your job, is making that job more long than the time you predicted when you was filling the week of work and appointments.

I also do some exercise with the body when I wake up and some meditation to increase concentration and awareness. That help you to stay more focused and to be more kind to yourself.

I could tell you more things, but doing these are a lot of habits to change and is not easy at first!

But I assure you, by doing this you are focused a lot more than before.

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The second part of this discussion is related to the now very common incorporation of machine learning (ML) systems, where a system is “trained” on a collection of data [ ASOS DESIGN Wide Fit oxford plimsolls in orange faux suede cheap new styles the best store to get official cheap online BMW4L2
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An additional problem is the way algorithmic outputs are actually used (or misused): while the system may say that there is a higher of one outcome than another, this “prediction” is not and must not be equated to destiny, though it’s all too easy to do so. Making important decisions entirely by algorithm is a profound abdication of responsibility; there needs to be a way to also bring human judgment, intuition and empathy to the situation. And, further, correlation is not causation. Overtraining and/or false correlation detection are also problems (Stephens-Davidowitz, 2017; O’Neil, 2017). I’ll also note that there are now many ML systems where the classifiers evolve in operational use, though I won’t consider the additional complications that these introduce explicitly here.

A third issue here is the high level of path dependency (or dependency on history) in many of the systems that consumers interact with regularly. This dependency means that “algorithms” cannot stand alone: they operate in a very complex and extensive (and often proprietary, unrecordable or even un-reproducible and unknowable) context. If you think about how Facebook decides what to show a user, the choices depend on not only the history of the user’s interactions with Facebook in various dimensions (frequency, duration, what he or she did or didn’t like, what Facebook thinks it knows about that user [Nield, 2017]), but also the histories of many other users of Facebook, particularly those who are connected by “friendship” relationships to our user in question. If you change any of these inputs, the “feed” (including the advertising stream) offered to our user will be different. While the “feed” may be algorithmically generated [ 8 ], in a very real sense it’s not meaningfully reproducible. Recommender systems have somewhat similar processes; think about the way Amazon suggests books, considering your own previously expressed preferences (both through purchasing and browsing, and ratings if you’ve offered them), in addition to the ever-evolving histories of people who share common purchases with you. Indeed, construction of “feeds” can be thought of as an application and extension of these recommender systems (which pre-date the emergence of social media platforms) to new environments; in fact, early work on recommender systems used the terminology “collaborative filtering” prior to their commercial adoptions by corporations like Amazon, underscoring the reliance on community in addition to individual history. Then think about the extent to which travel sites that offer various flight and hotel options may incorporate similar algorithms both in the ranking of their suggestions and in the pricing [ AFVandevorst cross strap sandals discount 2014 newest sale low price discount low shipping buy cheap footlocker finishline MAuaN
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