This is just absolutely amazing. Thanks for sharing @jamesrequa.
My personal experience about PhD is that there is some gap between academia and business here in Poland. I tried to enter the program twice but both times after initial talk with assigned professor I realise we are not speaking same language. Also there was no evidence PhD study gonna be better experience than just actively studying here and there and participating in different competitions and working on different projects. But maybe that was because of me as I am mechanical engineer and academia was looking for CS students or similar. Still, internal feeling says me if I have an opportunity I would work on PhD.
Just thought I’d add a few of my thoughts after some excellent follow up posts above:
I think quite a few people are probably feeling this way at times as well (me included).
But several things keep me motivated:
I have overcome many (many) obstacles and problems that I didn’t know how to solve, and generally found that it’s just a matter of time. It may be some programming problem that takes a few hours or days to work out, or for example trying to work out a few years ago how html, css and javascript worked to build web pages (that took me weeks to get comfortable with). When I get stuck I tell myself - youve been stuck on things before and you kept chipping away at it and you worked it out. Keep working at it and you’ll solve this as well.
The way I have been tackling this course, as someone with a full time job and family, is to dedicate all my free time to this course and material related to just the part of the course we are working on. This involves: listening to relevant part 1 lectures on my ride to and from work (part 2 repeats shortly), reading the fasi.ai forum in my down time after work, and reading relevant papers and blogs mentioned in the forum after family has gone to bed (Im too tired during the week to fire up my machine to do any coding), and keeping print outs of relevant DL papers handy at home for when browsing on a device is not the right thing to do.
With regards to notebooks, the general consensus I have read here is to reproduce the nb from scratch. I have been trying at least to reproduce from notes the sections I am familiar with - eg model, transforms, learner) and re-typing out the bits I am not as familiar with - until it does become familiar, which may take a few repeats.
I have found that the bits I got stuck with when repeating part-1 in prep for this course, and the bits that weren’t fully covered - like submitting to kaggle, were the parts that when I re-listened to the lecture (on my way way to work) - I then thought - hah I already know that.
Lesson 9 covers so many new (to me anyway) concepts that Im going have to break down into pieces and iterate over a few times.
That’s a great share @jamesrequa. I was there at the Intersect on Tuesday and some of the talks were particularly useful. While Soumith’s was fantastic, personally I found the “Competing with Skills, Winning with confidence” super relevant and very informative. There were so many key takeaways. For candidates who are job hunting, switching domains, aspiring to get better at their own niche domain etc. I’m planning to write on on it but here’s a brief of what they mentioned.
Whenever you don’t understand something, mapping it back to the domain you know and understanding it from first principles always helps. (Elon Musk is a big proponent of thinking in first principles).
Even super-confident people sometimes do not know what they’re talking about. It just means they know a few things about something, certainly not in its entirety but most importantly they are quick to course-correct incase they see a mistake in their understanding.
Part of confidence is failing. A lot. (Jeremy always mentions this. 99% of the time the code doesn’t work but after a while, you’re sure of what works and what doesn’t and understand the concept well.)
Find self trust. It’s key to being open and gaining confidence over time.
Get a lot of feedback. Why do you think the greatest of the athletes / teams have coaches? Because, it just works. Feedback is the most important experience in learning something. (We Machine Learning guys surely know the value of feedback)
Ability and humility to ask.
Have a growth mindset instead of a fixed mindset. (Growth is always dx everyday, however it always amounts to something really valuable over time)
Getting a job is a marketing exercise. Write blogs, GitHub is your best friend, create your own portfolio. More importantly, be authentic.
Be an excellent story teller. They recruiters are not trained to connect your dots. So, figure out your story and present it in the best way possible.
Shorten the distance to your dream company: Figure out the engineering blogs, identify the team members on Twitter, talk to them about any interesting insights you found on their blogs, and establish meaningful conversations. Don’t blindly run behind recruiters.
Evaluate the work and team you are dreaming of. Be critical.
It was super nice to read the authenticity moment of Aditya and all your insights and reassurances.
It inspired me to ask the following questions. Is anyone else struggle with this, and how do you fix it?
I get a better intuition/understanding of the code whenever I write it down (with pen and paper). Of course if I have any puzzles I will try them coding them, but then again I will return the results to the pen and paper.
So this applies also when taking notes, I will write down everything that seems important
I tried replacing it with type-writing in the doc - but it’s not the same
All this process makes me think that I’m a slow learner, and I need too much time to understand/memorize things.
IMO this all about focusing and concentration. Keyboard writing I would think “easier” than hand writing (plus it is harder to draw on the fly in MS Word). I.e. it is easier to type and think about something else, than handwrite and think not about subject.
When we think about one thing while doing something else, it is always our thoughts, which are the focus of attention. This suggests that there are at least two thresholds, the higher associated with overt movement and the lower with thought. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4879139/
But hand writing is slower obviously.
What worries me as well that in current world pace - “old school” methods to digest huge amounts of content becomes too slow, and it has to be some more “modern” and revolutional way to keep level of focus and comprehension.
Being an Engineering student, If you can make your own self-written notes, you know exactly what is to be found out where(so it actually saves Time), there can never be substitute for that because the feeling when you write down something with a Pen is awesome, the same isn’t with Typing the same on a doc…
It might seem a complete waste of Time initially, but it isn’t… Scribbling on Paper’s is what we learn first…
Recently itself i found a lot of cool things bash shell can do it for you, Everytime it was usually copy and paste but now the things are completely different, If i wont type them, then i will never remember them no matter what’s the length of the command, I prefer typing now…
If you feel low on confidence,then
Just to re-mind you “Pen is mightier than Sword…”
This Quote Block from Radek
---- When a true master of a discipline performs an action, it seems effortless. Consider great pianists oh how easy it seems to just sit in front of a piano, close your eyes, sway a bit and the music just flows. But then we go home and it doesn’t seem to be that easy.
How do I keep going? Understanding that what Jeremy teaches is a culmination of years of expertise in the field. Trying to sift through all the courses in 7-8 weeks (Part 2 Especially) is a bit of a stretch. Might take months or a year to get through.
Learning on a know-how basis. Instead of working on assignments/problems given in notebooks. I try to look at various competitions to implement what has been taught. Example: To learn bounding boxes, I’m going to take ongoing kaggle competition to find where the clothes are.
Have a much bigger goal:Important: When you have a much bigger goal that you want to achieve with deep learning, your approach towards viewing difficult(presumed to be difficult) things change. You develop (my opinion) a change in viewing things to “if we cannot comprehend this, how can we achieve our bigger goal?”
Aside from the fantastic course materials, one of the key takeaway I about this course is “learning how to learn” and improving my learning process along the way by learning from others through their super nice posts/articles/blogs in the community and in this forum. It’s really inspiring to read and keep me stay on the course when I am struggling.
5 months ago, I bite the bullet and quit my full-time job to master the material with the goal of getting a job in the “Software Engineering 2.0” field. Prior to that, I am studying much of Stanford’s MOOCs and fast.ai first version of the courses but while I did get stuff working and got results, I was left unsatisfied. During that 5 months time-frame, I stayed focused and relearned every single fast.ai lessons. I watched the videos so many times (at 1.5x to 2.0x speed, jump around the video using the time code and text transcript). Along the journey, I work on the assignments throughly, dive deeper and deeper into theory by reading papers, read articles highlighted in the lessons, writie my personal notes, practice on my own project (most important), and joined study group mainly to teach (as a way to solidify my knowledge). I never expected that I really need to study this much to achieve what I intended. And the learning continue. So, never give up and don’t underestimate the time commitment.
about this course is “learning how to learn” and improving my learning process along the way by learning from others through their super nice posts/articles/blogs in the community and in this forum. It’s really inspiring to read and keep me stay on the course when I am struggling.