When successful people say they're not the best at anything, what they really mean is that they are not the best alongside any of the dimensions that we humans have a name for and that we can easily quantify (running 100m/chess/comp. programming/traveled the most/h-index...)
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But if we were to quantify "bestness" as the length of the resultant vector across all of the relevant dimensions - then the scoreboard changes.
They are usually, all in all, the most well-rounded professionals.
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That's why we don't have a global scoreboard for "the best entrepreneur", "the best leader", etc.
These roles have an immense breadth and it's impossible to quantify and rank those people.
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Try and quantify charisma, emotional intelligence, people skills, management skills (time/money/people, etc.), sales/financial/legal/tech know-how of a single person, and spit a single number out.
Not going to happen.
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People usually use the net worth as a proxy for how successful somebody is - but that's, I think we can all agree, a poor metric because some people give way more than they take in return.
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Not to mention that it fails to capture whether the impact they had is net positive or negative for the society as a whole.
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Inspired by @jockowillink saying he's not the best at anything, whereas in fact, his combination of physical and mental stamina, EQ, coupled with amazing leadership is what makes him one of a kind.
We just don't have a scoreboard for people like him.
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In this video, I build an MLP (multi-layer perception) and train it as a classifier on MNIST (although it's trivial to use a more complex dataset) - all this in pure JAX (no Flax/Haiku/Optax).
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I then add cool visualizations such as:
* Visualizing MLP's learned weights
* Visualizing embeddings of a batch of images in t-SNE
* Finally, we analyze the dead neurons
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[🔥 Learn ML for beginners 🥳] I recently said I'll be binge-watching fast.ai's Practical Deep Learning for Coders and I did, here are my final thoughts!
I'm mainly going to contrast it with @coursera's course as that's the course I took back in late 2018.
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Verdict:
If you're in high school or a student or more precisely somebody who still has difficulties creating your own learning program (no experience with self-education) I'd recommend you take @coursera's course - it's more streamlined.
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You'll know exactly when to read, watch, or code.
On the other hand, if you already have some experience (you had some tech internships/jobs) or you're considering switching careers (again you're experienced) or simply integrating deep learning into your own domain...
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Again thanks to @PetarV_93, @relja_work, Cameron Anderson, Saima Hussain for being supportive throughout this journey!
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In this blog you'll find:
* The details on how @DeepMind's hiring pipeline is structured.
* Many tips on how to prepare for top-tier AI labs (like DeepMind, OpenAI, etc.) in the world (for research engineering roles but I guess many tips will apply for scientists as well).
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I'll be binge-watching @jeremyphoward and @GuggerSylvain's @fastdotai "Practical Deep Learning for Coders" course today and tomorrow! 8 lectures, ~2h each. It's going to be fun! 😂 Why?
Well:
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* I want to update my blog on getting started with ML from 2019 where I only recommended @coursera (and I realized just how bad my writing was just 2.5 years ago!).
I recommend you bookmark it but don't read it just yet, it should be ready by the end of this week!
* I want to be able to give better advice to "younger folks" in general. I get a lot of questions on my Discord as well (join it if you haven't: discord.gg/peBrCpheKE).
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* The problem of catastrophic forgetting (babies face it - that's (probably) why you don't have any memories of when you were really young - and AIs face it). How do we go about cracking it?
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* Embodied AI - does an "AI" have to have a body before it is actually intelligent? It seems our body is a distributed processing system.
As an example, they mention the shape of the ear canal and the fact it does some form of Fourier analysis in real-time ...
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