And this is my last blog post in the 8 week series 
Of course, many doubts about both the content and the presentation. I think I should have asked people to take a look at the post before I published it. But all these coulda shoulda woulda are probably also a direct result of the insecurity about posting things that despite having done this for 8 weeks hasn’t completely gone away.
But oh well. If you cannot conquer it, maybe at least you can ignore it
Are you a Writer?
Truly your writing skills are awesome…
Nice post
Thank you 
I am not a writer. And for most of my professional life my writing sucked. To the point where I had it in my year end objectives for a couple years running to figure out how to be more concise I never did before leaving that job 
I do put a lot of work into writing these articles though. Really glad you enjoy them
I have to confess- I never took blogging seriously. My first article was a really bad attempt. But @radek has been a constant source of learning for I think all of us, and I think I can say I’ve improved a lot-thanks to him
I think this is the best part about fast ai too. We get to meet great DL folks.
I’ve learnt so much in the past 6 months just by scrolling through the forum threads daily, more than what my school has taught me during my 3 years of undergrad studies.
ooo…Does anyone remember the moment from Part1v2 where Jeremy had posted a screenshot of the LB?
And the LB got turned upside down every 2 hours?
Hi, I just wrote a blog to explain the intuition of Decision Trees/Random Forest.
P.s.reposted here as I did not see this thread yesterday.
I’ve posted a few blogs here on setting up AWS, jupyter notebook. Feedback welcome. Tutoring a local group in Brisbane, found it easier to blog on medium than demo the same thing multiple times.
suggestions on best option to assign a static ip (elastic IP on aws), route a domain to the ip and create a ssl from a recognised ssl provider welcome.
Hi all, I have begun blogging and have written a couple of blogs. I would like some suggestions on the things I can improve (especially since the first one is based off the first deep learning class).
Augmentation for Image Classification:
I’m planning to put together a series of best practices from all the resources that I come across, so this is the first one:
@jeremy your feedback will be most valuable.
Thanks a lot!
For weight initialization you can take a look at here http://pytorch.org/docs/master/_modules/torch/nn/init.html, for references and maybe further talk about which initialization is needed where, e.g. https://arxiv.org/abs/1504.00941.
Thanks for sharing @neerjadoshi In the augmentation article, note that training time augmentation only helps when you train multiple epochs. In your case you’re just training one epoch, so I’d guess the improvement is just random noise. Also you need to unfreeze the network, otherwise over-fitting is very unlikely (and therefore augmentation doesn’t help).
I’d suggest trying a dataset that’s not quite as similar to imagenet, so needs more epochs to get good accuracy, and you should be able to show a more compelling difference then.
The initializations post is looking good. I think it would be helpful to show code examples of each concept you’re talking about; and where you do show code, put it in a code block, not a picture, so people can copy and paste it and try it out. Also, maybe show some experiments to show how it impacts training in practice?
A link to the papers that introduced each init method might be nice too.
I realise what can be corrected in the augmentation article. I’ll find a suitable dataset and train multiple epochs to capture the difference in performance.
Also, I’ll update the code snippets and references in the second article. In the material I’ve been reading, I’ve also been finding a lot of small nuances to add to that blog, so I’ll update accordingly.
Thank you for your feedback!
I was 99% satisfied with all blog posts I found about Resnet (for example the great blog post “Decoding the ResNet architecture” from @anandsaha), but not at 100%.
Then, I searched in the Web and found, I thought, this 1% in the blog post “Understanding Advanced Convolutional Neural Networks”.
In the Resnet paragraphe, the autor (Mohit Deshpande) explains the beauty of Resnet in 3 points :
You can even forget the vanishing gradient problem and just look at an image of a Resnet network : the identity matrix transmits forward the input data that avoids the loose of information (the data vanishing problem).
That’s it 
Hi all, I just wrote a blog post for entering the StateFarm competition with fastai v2 (PyTorch) library.
As I am trying to be familiar with the library, I try the StateFarm dataset which is cover in v1 but not v2 and summarize my experience here.
I got a little adventurous and set up Ubuntu 18.04
I have written a little guide on the tiny tweaks that make everything work.
Please let me know what do you think about it.
Sanyam.
Good article, maybe i am missing something, but perhaps you could add to your post the process in detail for getting a twitter follower assuming you have zero?
I got 1700+ followers by just repeatedly posting and sharing posts on Twitter and some relevant Facebook groups.
Ok thanks, method 3) repeatedly share posts. This seems to be easiest of the 3, ill experiment
My first blog post about deep learning – feedback very much appreciated!
this is an awesome blog post!
Hey guys, I just wrote my first blog post which is an introductory post to Pytorch, In this post I talked about torch packages, tensors and how does automatic differentiation work, along with a hands-on Pytorch example.
I would love to hear your feedback. Thanks 
Manal elaidouni
Hello, I am currently interested in VQA so I wrote a summary of the paper, Kafle and Kanan (2016), on available VQA data sets and models: https://www.yjpark.me/blog/jekyll/update/2019/03/11/notes-on-vqa.html
More recent VQA models are also summarised here: https://www.yjpark.me/blog/jekyll/update/2019/06/10/recent-vqa-models.html
I would truly appreciate your feedback. Thank you in advance.
