What I will focus on to succeed in this course

This is not even close to top 10% and as my data center AKA my parents house gave out (power outage till evening at least) this is likely going to be my final submission :slight_smile:

A simple voting ensemble I somehow remembered reading somewhere about and cooked up quickly in the heat of the battle. 4 models - the best being densenet201 achieving ~0.62 on the LB. Trained only the classifier part :slight_smile:

Above all, despite the relatively poor results, taking part in this felt really good and I had a lot of fun :slight_smile:

Here are the main lessons I learned:

  • Always start with completing a full pass all the way to submission as early as possible. What I mean by this, is that it is extremely crucial to have a bird’s eye view of everything that you will have to deal with. You cannot assume things - likely this activity will uncover quite a few things you have not anticipated and that might be harder to deal with (or impossible) to deal with effectively if you sink a lot of time into perfecting the earlier stages of the pipeline.
  • It’s all about IO with datasets large relative to your HW. Just reading the 200GB of data of my HDD takes I believe over half an hour! If I ever again work on something this size, I will want to put some serious thought into RAID0, getting an SSD, more RAM, ways to preprocess the dataset, etc.
  • Frequent the kaggle forums especially for the competition you are taking part in! Such high quality posts in there and a lot of good pointers how to attack a problem!

All in all, I find that working on this was time well spent :slight_smile: But I feel it is important to not lose momentum. Would love to participate in the icebergs or the favorita competition, but the wise choice is probably revisiting collaborative filtering, coding up those RNNs and rewatching lectures :slight_smile: So this is the direction that I will try to point myself in :wink:


An SSD is absolutely necessary for good performance with DL - ideally an NVMe.


Very well said, this is so true!


@radek I stumbled upon this post today and I can totally relate. Good job on tracking what not to do.

I have been keeping track of learning priority list(not super organized but hard to focus without having one). Generally, I am alternating between time blocks of learning to blocks of doing. The only thing I would say is to treat it as a marathon and update your plan as you learn what else you need to learn :slight_smile: Would love to follow how your plan is going.

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Wholeheartedly agree :slight_smile:

I tried keeping a list as well a couple of times, but I would quickly fill the list up with a lot of things I thought were essential but really weren’t. Now I just keep a mental list of what to work on - this way I naturally revise what is on it by trying to remember it. I am unable to have too many things on it (usually one or two :wink: ) and things naturally fall off it without me even noticing :slight_smile:

I have two little kids and a full time job as a rails dev (complex, legacy app) and my recent realization is that I have been going at this deep learning thing too hard. I should not be staying up till 2 AM and this combined with the observation that it’s rarely the issue that we genuinely do not have enough time but rather that we give time to the wrong things means I have to make some deeper life changes hopefully sooner then later :slight_smile:

It seems quite obvious at this point that data science related things is what I want to do to greater or lesser extent in life, one way or another. Something strange is also happening through a couple of months of following Jeremy’s and Rachel’s advice very closely - I feel with enough time I am capable of tackling nearly any problem at a level that I think could be quite useful to potential employers. Not sure I have enough to show at this point to convey this to them though and also not fully convinced that doing this professionally at this point would be the way to go.

But I digress - I am currently working on making a deep learning submission to the favorita kaggle competition. Another lesson learned - I will not join a competition in forseeable future just a month before it finishes! Too little time to get to anything fun, okay-ish for learning but not compatible with my schedule at all. 11 days to go only so would like to finish this. I then have a couple of posts I already started that I would like to complete, including beginning a lessons learned from fast.ai lib series. I feel that those posts can be quite good so excited about getting them done, and at least writing down what I want to write and getting it out the door finally will be a good thing :slight_smile:

And that is it really as for my plan. Once I make progress on those posts, I will either hop into a new kaggle comp if one launches, or will focus on finishing v2 lectures, or will write more posts including something fun on random forests, or will read the pandas book (Santa got me it for Christmas :slight_smile: ) or the DL book by Chollet (I really liked the first couple of pages) - most likely will do some combination of the above sticking 98.9997% to fast.ai materials :). There is still the lin alg course I wanted to do. the 1.0003% of time will be given to reminding myself what works and not straying away too far as there are still so many fast.ai goodies I haven’t taken a closer look at.

As a side note (I think a lot of people might find this useful), there is this great podcast episode with creator of Ruby on Rails, David Hainemeier Hanson. He has a lot of good things to say about productivity and learning, I think I should listen to it again :slight_smile: He highlights the value of uninterrupted code sessions and turning down noise in general. I got off Facebook entirely and I am amazed how much can be achieved via giving something uninterrupted attention for some period of time. Need to figure out how to use Twitter better (maybe unfollow a bunch of people / don’t randomly jump into reading it but check it a couple of times a day from my computer only). But a lot of good stuff there from David, would highly recommend this talk and in general his approach to programming (very welcoming to people new to coding and in general from various walks of life, have benefited from listening to him greatly myself).

Sorry about this disorganized post - will try to write a more coherent update speaking directly to the plan I outlined in earlier posts and how it is going sometime down the road, maybe once I have more to show in terms of posts, etc :slight_smile:

Kids are slowly waking up so time to get the older one ready for kindergarten and in general get things going around here :slight_smile:


That’s the longest post I have read till now…

You are truly an amazing writer…



The battle was valiantly met at the favorita grocery hills where the armies of general Radek suffered a spectacular yet painful defeat! :smiley:

I ultimately finished 1117th.

So many lessons learned from this competition - most surrounding the process of approaching a machine learning problem and the data processing part rather then the model construction itself.

The fact that Kaggle launched the new data science bowl competition just now has the potentiality of messing with my plans, but I think I’ll take it very slowly with it and will focus on test driving my general approach and playing around with ensembles.

The main focus going forward will be on working through Pandas for Data Analysis. I also will stat publishing blog posts about what I learned from studying the fast.ai library - have a couple already nearly finished and ideas for more.

It is quite easy for me to post here on the forums since this is quite a friendly space where I feel at home, but for various reasons probably I find it not so easy to tweet. I would like to change that and so I am starting my very own 100 days of Twitter challenge where each day I will tweet something and will comment on someone else’s tweet. The timestamp on this post will serve as a nice way of checking how many days have elapsed :slight_smile:

Oh yeah - and if I ever post anything to these forums, which knowing me I most likely will :wink: I will strive for conciseness. This is another dimension along which I feel I should improve.


good luck! writing concisely is difficult - i used to write tech doc for a living and those rules from Altucher really helped me: 33 Unusual Tips to Being a Better Writer


Helena - thank you. This was so good. I feel I will come back to this post often in weeks to come.

I tried reading the first edition of Pandas for Data Analysis, didn’t like it much - I prefer Python Data Science Handbook by Jake VanderPlas. It’s available free online, together with accompanying jupyter notebooks Python Data Science Handbook

Also, datacamp has some great courses on pandas like manipulating dataframes or working with time series data in pandas
Here’s a link for one free month of subsription:


I have Chollet’s new book and have read first 5 chapters so far. its a good book with some useful tips. however most the content is already covered in this course.

I think he is not a native English speaker, his explanations at times need improvement.

PS: He uses Keras so the code from book cant be directly if you are using Pytorch.

Great materials there @malrod - thanks for sharing :slight_smile: I also found the v1 of the Pandas for Data Analysis not that easy to follow, but v2 is much, much better.

Adding the materials you shared to my mental todo list.

I only started reading it on my mobile phone when I have a bit of downtime (like carrying my toddler around :wink: ) - it tickles my brain in a nice way and I like the way he introduces various concepts.

Really well written and I enjoyed his insight from the intro chapter how a deep learning model is essentially a successive transformation of data into more and more useful forms.

Thx for the feedback @cynosure - wonder if the next chapters will make me like the book less or more :wink:

Thanks for the feedback- maybe I’ll give that new version of Data Analysis book a try.
As for Datacamp - don’t put it on a wait-list :slight_smile: You can squeeze it in-between more serious stuff. Exercises are divided into self-contained segments, so any time you have 10-20 minutes of free time, you can dive in. Unless you find such format of learning tiresome (there is a lot of hand-holding and it gets boring after a time) - it may turn out you’ll hate it after first 5 minutes :slight_smile:

And I also join the fanclub of Chollet’s new book. I just finished chapter 6 on working with sequences. The material was all new to me, but the explanations were excellent. And the content seems indeed the same as covered on fastai part1 (I only watched the first 4 lectures). I hope this approach to teaching DL gets more popular - teaching DL using python instead of showing equations on slides is still a revolutionary idea.

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+1 on Chollet’s book - brought me from ground zero to the running deployable project in less than several months - went deeper than most books, even the earliest releases - he is a perfectionist!


I do not shy away from a challenge, but this hasn’t been working out that well for me. I need to focus on less and not more.

What I am planning on doing is publishing a blog post each Monday over the course of next 8 weeks. Those will be blog posts from my Lessons learned from studying the fast.ai library series which are long overdue :slight_smile:

It just so happens that the 9th Monday from now would be the one when part 2 v2 of the fast.ai course is getting launched - ofc it is a big if whether I will get in, but no harm in being prepared and cleaning out my schedule :slight_smile:

I am now working on a post that is slightly more personal. I had a couple of sentences but I threw them away and have been trying to follow the advice from the post that @helena linked :slight_smile: Ah we will see how it will go - there is a chance of me making a complete fool of myself and on the other hand maybe it will be reasonably good. Guess we’ll have to wait and see :slight_smile:


I posted this to Twitter, but then I think I made a mistake in quoting and it changed the meaning so I took it down.

I like the phrase so much I still want to share it somewhere.

I was not interested in making a lot of money, I was interested in doing a lot of living. - Dale Carnegie

So yeah, this is precisely how I feel about ML :slight_smile:

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Are you a writer?

Well the first trait matches…

All the best…

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Responded in the other thread with my best guess but not sure if correct.

The first Monday blog post is out though the path to it has been quite turbulent.

I would like to publicly thank @init_27 for his support! Initially the post was not getting much attention neither on Twitter nor on Medium. I don’t think I tweeted it out properly and it needed a bit of a clean up to which I got around only today (Tuesday).

@init_27 not only showed me a lot of encouragement but he also thought the post was good enough to submit it to hacker noon, where he apparently knows people :wink:

What a big difference a colleague from half across the globe can make :slight_smile: