I started out as a Software Engineer, then switched to Data Scientist. However I have decided to switch back to Software Engineer again now, simply because I enjoy the work more. So I would advise to try get into the nitty gritty work of both, preferably through internships, and then decide which route you prefer.
I would also suggest to apply for a lot of them anyway, there's not enough skilled and experienced people to fill all positions right now, so some of them will recruit more junior people than they might be looking for at first.
Not sure I'm reading you right, but here are my two cents.
Also living in Scandinavia and I would disagree with this advice. To be dependent on the welfare system is not exactly being free and have security.
Like the Swedish Prime Minister Göran Persson said: "One who is in debt is not free".
And living off of the welfare system is in my view to be indebted to society. I would not be able to shake that feeling if I was, at least not if it was voluntarily. Add to that the social stigma, even from close friends and family. This would limit your freedom of having an agency in social interactions.
Also I would not consider it secure, since the rules of the welfare system changes quite a lot over time. Which you have no way of impacting, meaning you are dependent and not free.
EDIT: Just realized the question was about "security+free time". I guess you would have free time, which is not exactly the same as being free in any meaningful sense.
EDIT: Not sure why I'm being downvoted. But for clarification I can add that I think this applies if you voluntarily would live off of the welfare system, thus leeching from the ones who really needs it. It's of course a totally different story if you are involuntarily need to get welfare to survive and live a decent life.
While those are valid points, I don't think the idea is "plan to live off the welfare system" but rather "take the risks and try something new", because if you fail you have a safety net to catch you and help you get back up again.
Totally agree and I would argue that there even is financial gain for society in letting people take such risks. Maybe I was kicking at an open door, but my point was that there, in my opinion, does not exist a huge incentive to misuse the welfare system voluntarily.
This was funny! I recognize these conflicting views. The first is the view that math is a fundamental force governing nature. The second is that math is simply a way to describe the forces of nature. I tend to agree with the second view and are often dragged into arguments which has the roots in this difference of viewpoints.
I'm not sure if either of your arguments applies in this case; the argument I was trying to make is that the colloquial usage of the word "space" predates the mathematical usage, so claiming that the word "space" in a colloquial context comes from the mathematical context is inaccurate. (This is a purely historical argument and not one of philosophy.)
- Get listed in any/every saas (esp b2b) directory I can find (haven't done producthunt yet, though).
- Send out cold emails (not a lot so far). I'd start making cold phone calls if I wasn't sitting at a day job during business hours every weekday. I could try postcards..?
- Post to online formus - kind of like I'm doing here :)
I'm really trying to avoid resorting to PPC ads, but it might be time to do that.
I'm also thinking of making it free for academic use, just to get some folks using the product and talking about it.
Also, the thought to find a partner who knows bizdev/marketing/sales has occurred to me.
Thanks! I think 30-70 visits per day without paying for them is quite a lot after six weeks. I'm in a similar situation, working a day job while trying to get something off the ground. Right now I'm just experimenting with ideas and trying to learn as much as possible.
I think paid ads is a pretty good approach to validate the product and copy, even if it's not a viable strategy in the long run.
Making it free for academic use makes sense in more than that sense. If you're lucky you might get a .edu backlink from it, which is valuable for seo.
Also, I would consider increasing the price quite a lot. If this really increases sales people are willing to pay a lot more. That would also mean that you can take the effort to do direct sales, whereas now the ROI for manually aquiring a customer is too low. Good luck!
Technically yes, most often it's about stacking more layers in neural networks, making them "deep". However, there is some merit to the new hype since stacking more layers worked way better than anyone previously working with neural networks and ML thought it would. But in theory you could generalize deep learning to other methods than neural networks, it's basically about creating way more complex models than those used in previous research and feeding them lots of data. Thereby assuming less about the problem and letting the model figure it out.
> it's basically about creating way more complex models than those used in previous research and feeding them lots of data
Those are instructions for over-fitting. Deep learning neural networks escape from this problem somehow, but it's not a given that other models would escape it too.
This is true! Overfitting is definitely one of the biggest problems with deep learning. Some techniques to avoid it have been developed, such as dropout (introducing noise) and early stopping. But in general this is why deep learning requires huge amount of data, a deep learning model will overfit if not given enough data. That is also why (at this time) it only performs well for certain problems where the ratio between available data and problem complexity is high enough.
The traditional way to avoid overfitting is to reduce the number of independent variables, shrink coefficients towards zero, or otherwise limit the complexity of the model.
With deep neural networks the approach is different. Instead of trying to find global maximum (which is too hard, and will also cause the model to be grossly overfit), the algorithm stops much earlier. Such "underfit" models seem to generalize much better.
They mostly escape from that by using huge amount of data and massive computing resources. Deep learning was became feasible because of the huge amount of data companies like Facebook, Google, Apple and others has collected.
Deep networks have more layers than the previous generation. They seem to work better in engineering practice than mathematically equivalent short wide networks.
We moved from slack to hipchat at my old job because it was cheaper. Hipchat is just a terrible product, both on iOS and in the browser. Don't even know where to begin, it was just a pain to use and every employee I talked to hated it. I'll just give a few examples:
- Unreliable notifications. Sometimes it notified you about messages sometimes it didn't, no idea why.
- You could get a push-notification about a message, then open the app and have no clue who had messaged you or in which discussion/group.
- Emojis had really unintuitive shortcuts. I remember people used to send some kind of skeleton dancing all the time, because it's shortcut was a (Y) (which is commonly some affirmative/"yes" emoji like a thumbs up).
I agree, the numerical results are poorly presented.
What was measured was wether their tests can find a dominant eye in the subject or not as well as the difference in Maxwell's centroids between both eyes.
Eye dominance results:
They had two types of test for this; the sighting test and the after-image test.
For the control group they found a dominant eye in 28 of the sighting tests and 30 in the after-image tests. In the 28 cases where they found a dominant eye with both tests, the tests corresponded perfectly, i.e. if the sighting test indicated the right eye was dominant the after-imagetest also did.
For the dyslexic group the sighting test found a dominant eye for 14 subjects, and the after-image test for 3. In the 3 cases where they found a dominant eye with both tests, the results corresponded perfectly.
Maxwell's centroids result:
For the control group they showed that in 29 cases the asymmetry between the centroids was at least above 0.3 (as far as I can tell from the figures), where 0.3 means "weak asymmetry" and 0.6 means "strong asymmetry". One case was slightly above 0.2.
For the dyslexic group 27 cases had ~0.0 in the asymmetry measure. 2 above 0.3 and one slightly below 0.3.
I have generalized a bit and labeled results ranging from 0.3 to 0.5 as "above 0.3".
Thanks! It sounds like an interesting result, presumably there will be fairly rapid efforts in the field to replicate these findings. (I'm influenced by the fact that this was published in a decent journal.)
In order to have some sense of how these findings relate to the notion of "dyslexia" as used in public life in western countries we obviously need to look at how the case cohort was selected. They say:
> all encountered difficulties in reading, spelling, writing and recognizing left from right
Presumably that means that every one of the 30 had problems with all 4 of those tasks. Possibly this is a narrower definition than that used by the public / educational psychologists -- I think they often diagnose dyslexia without requiring left/right confusion as a symptom?
I would also suggest to apply for a lot of them anyway, there's not enough skilled and experienced people to fill all positions right now, so some of them will recruit more junior people than they might be looking for at first.