DOI/ISBN: 978-1138210608
URL: https://www.amazon.com/Ethnic-Marketing-Practice-Entrepreneurship-Routledge/dp/1138210609
Hi, could you please find this book asap for my research paper? I'd be so glad if you did. Thanks x
Print ISBN: 9780357033890, 0357033892
eText ISBN: 9780357033203, 0357033205
email me at itailexpert@gmail.com to get the ebook pdf
Hey, im looking to take one of these modules this year. What are you experiences, how was the difficulty, how was the lecturer, would it be useful for someone interested in founding startups? Also what are the differences between these two modules? They seem really similar to me.
Thanks in advance!
I've been searching for this book all day with no luck. Any help would be greatly appreciated !! Entrepreneurship in Theory and Practice Paradoxes in Play, Second Edition; Suna Løwe Nielsen, Kim Klyver, Majbritt Rostgaard Evald and Torben Bager Hardback:978-1785364457 / 1785364456 Softback:978-1785364471 / 1785364472
Will someone please help me find this? Thanks sooo much!
ISBN: 9781544354620(paperback) or 9781544354668(looseleaf)
looking for entrepreneurship the practice and mindset 2nd edition pdf
Literally on any sub Reddit, if you have an opinion that’s not even unpopular, but just slightly outside of the norm, you are immediately downvoted. For example, subreddits that are centered around movies or shows. If you have an opinion or observation about a popular show, and it amounts to speculating on alternative plot points, you will automatically be down voted for no other reason than the users feeling like their favorite show is being threatened. Like, how dare this person think up an alternative plot for this amazing show.
And the worst part is, it’s almost all exclusively dumb. When you look at the mob position, it’s almost always the case that it can be refuted with a more intelligent perspective. But on reddit, 50 users saying “Durrrr” is more valuable than intellectual and well-thought out ideas.
This site is a great place if you enjoy mob mentality and being part of a collective. But it’s a bad place if you are an individual who is unique.
In this post I am thinking only about MLPs with ReLU activation function.
The default pytorch initialization for linear layers is from a uniform distribution centered at 0 whose limits values depends on the input dimension. Many papers assume initialization from a Gaussian distribution with 0 mean and provide a certain variance.
There is also this work [Pennington (2017)] that proposes orthogonal initialization to achieve what they call dynamical isometry which means that the input-output Jacobian is 1 (or stays near), which in turns implies that gradients do not explore nor vanish.
There is also this result [Hu (2020)] that shows with orthogonal initialization one can achieve linear convergence (convergence that decreases as k^t with K<1 and t being training step) for linear networks with depth being independent width, whereas for Gaussian initialization width has to grow linearly with depth to achieve the same convergence rate.
From the preliminary scouting, it seems the three most popular initialization schemes for MLPs are Gaussian, uniform (both centered at 0), and orthogonal initialization. Is there anything I am missing?
From my experience when playing with MLPs using pytorch default initialization didn't work very well. For deep networks, at some (deep enough) layer the output to all inputs would be just 0 (or 0 in most dimensions). For that reason, I would always just add a little constant to push weights away from 0, so that ReLU would not zero everything. I was doing the same with Gaussian.
I'd like to hear people's opinion on the following (please recall I am asking from the context of MLPs):
- What hack/tricks do you use for initialization when training your NN? does it matter?
- I mentioned some results that seem to be well-known, but surely I missed some. Are the results on initialization that you'd like to share? either other completely different initialization schemes that were successful, or some theoretical result?
Any comments/thoughts/points that add to the discussion of initialization schemes are welcome. :)
