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Practical Deep Learning for Coders 2022

fast.ai

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Re: Practical Deep Learning for Coders 2022

#51

Post by qwrshr »

gandalfgreybeer wrote: post #36
mkl wrote: post #32

Earlier quoted context omitted.

How many hours do you think this course would take for an experienced developer with plenty of applied maths but ~no machine learning? How easy is it to do the course on my own hardware rather than cloud notebooks? Would that make it closer to practical deployment?

Not him nor will I talk about his course, but I’ve been in the field a reasonable amount of time (both on the academia and industry side). Honestly, applied maths will get you a long way and make it easier to digest the concepts (you might just see them as repackaged problems depending on your mileage). If you have good programming skills and discipline you practically have most of what you need. Re the course, I jus…

out of curiosity, how much applied math should one bone up on? (Obviously the more the better, but diminishing marginal returns and all that.)

Re: Practical Deep Learning for Coders 2022

#52

Post by rg111 »

victor106 wrote: post #48
rg111 wrote: post #40

Earlier quoted context omitted.

He says repeatedly "You don't need math", and stuff like that. This is not insulting. That man is my hero, and I deeply respect him. But his 2019/20 course was riddled with such statements. He repeatedly said that one doesn't need math, and showed tools like drawing math symbols on a website to learn their names and ride on that. No further math needed. It's like you can wing it in Deep Learning without learning Math…

Can you please provide some resources you used to learn?

Definitely do the fast.ai course. Totally worth it.

But also use ISLR, Goodfellow, Bishop, etc.

Start with Andrew Ng's ML, then do the first part of Aurelien Geron book, then do Ng's DL specialization, then do fast.ai. Then learn PyTorch. A great book would be Sebastian Raschka's book. Also d2l.ai. A fast-paced, but really good course would be the Neuromatch DL tutorials.

Then move forward based on your interests.

Yann LeCun has THE best MOOC on DL on YouTube.

For the Math, I majored in Physics, so stuff came naturally. I suggest Imperial London's MOOC on Mathematics for ML specialization, Robert Ghrist's Calculus course, VMLS book for Linear Algebra. For stats, haven’t found a good one yet.

What you read, how much- these all depend on what you want to do. Where do you want to see yourself, and so on.

If you just want to brag about DL and put it on your resume so that you can get a job writing SQL queries and make PowerBI presentation as a "Data Scientist", then the bars are low.

If you want to do some DL, then that is another league altogether.

You need to be able to quickly read papers, understand ideas, use those for your own projects or papers.

Makes sense?

Re: Practical Deep Learning for Coders 2022

#53

Post by rg111 »

sabertoothed wrote: post #44
rg111 wrote: post #40

Earlier quoted context omitted.

He says repeatedly "You don't need math", and stuff like that. This is not insulting. That man is my hero, and I deeply respect him. But his 2019/20 course was riddled with such statements. He repeatedly said that one doesn't need math, and showed tools like drawing math symbols on a website to learn their names and ride on that. No further math needed. It's like you can wing it in Deep Learning without learning Math…

I think you completely misunderstand his stance. You don't need the math in the beginning to train a model and get first results. Later, you will need the math and Jeremy clearly knows the math. He gives a great example: In sports, you don't start with learning about physiology and train individual muscles etc. (I paraphrase), you start playing basketball or baseball or soccer, and understand the overall game. And if…

It is good enough to not need heavy math to begin.

Yeah, I know.

But you need a lot of math to do Deep Learning.

But I do not think Howard tries to communicate that.

You can't show me people who knows high school math only and gets to work in FAANG, or PhD in DL/related, or CTO of an AI start-up, or anyhow "made it" in DL.

Re: Practical Deep Learning for Coders 2022

#54

Post by BeetleB »

wittycardio wrote: post #49
publicdaniel wrote: post #47

I am so grateful the FastAI team exists. It wasn't until I discovered their "Machine Learning for Coders" course that I really started to grok ML. I was in grad school trying to pivot my career from finance to data science. I didn't come from a computer science / math background and things just weren't clicking for me. I remember feeling angry, embarrassed, dumb, and overall that I wasn't smart enough to learn this s…

Quoted post unavailable.

Lots of people who've taken the Fast.ai course have similar things to say. It's commonly said it's the fastest way to get into DL.

Re: Practical Deep Learning for Coders 2022

#55

Post by hello34 »

BeetleB wrote: post #54
wittycardio wrote: post #49

Earlier quoted context omitted.

Quoted post unavailable.

Lots of people who've taken the Fast.ai course have similar things to say. It's commonly said it's the fastest way to get into DL.

Yeah just wanted to say, I am also one of the persons who has immensely benefited. So it may sound like paid, yet lot of people have immensely benefited like people from India, Nigeria, etc..

Check this article[1] to know a bit about philosophy of fast.ai and why it's so popular

[1] https://future.com/the-rise-of-domain-experts-in-deep-learni...

Re: Practical Deep Learning for Coders 2022

#56

Post by cinntaile »

qwrshr wrote: post #51
gandalfgreybeer wrote: post #36

Earlier quoted context omitted.

Not him nor will I talk about his course, but I’ve been in the field a reasonable amount of time (both on the academia and industry side). Honestly, applied maths will get you a long way and make it easier to digest the concepts (you might just see them as repackaged problems depending on your mileage). If you have good programming skills and discipline you practically have most of what you need. Re the course, I jus…

out of curiosity, how much applied math should one bone up on? (Obviously the more the better, but diminishing marginal returns and all that.)

None, just look things up as you go along if there is something you don't understand. You're likely not going to bother understanding how the optimization functions work or how the cost functions actually work anyway. They're implementation details in most cases.

Re: Practical Deep Learning for Coders 2022

#57

Post by jph00 »

thefreeman wrote: post #43
jph00 wrote: post #3

Hi folks - I'm the creator/teacher of this course. I'd be happy to answer any questions that you have about the course, learning deep learning in general, or the state of deep learning in 2022.

As someone who much prefers reading over watching videos, do you think I would miss much by just going through the book in the github repo? Or are those notebooks mainly supplemental to the videos.

I'd say it's the other way around - the videos are kinda supplemental to the book. The book has a lot more content, but doesn't have the interactive explanations in the course. Also the book is a couple of years old so is missing the more recent developments (but the principles haven't changed).

Re: Practical Deep Learning for Coders 2022

#58

Post by jph00 »

sooheon wrote: post #21
jph00 wrote: post #3

Hi folks - I'm the creator/teacher of this course. I'd be happy to answer any questions that you have about the course, learning deep learning in general, or the state of deep learning in 2022.

I remember you were bullish about Swift a few years ago. What's your current view on non-python deep learning?

I'm disappointed that Google shut down the Swift for Tensorflow project, because I do think Swift is a great option for deep learning.

In some ways Jax is almost "non-python deep learning" since it's treating Python more like a DSL for the XLA backend. Normal Python code doesn't work in Jax. It's a pretty reasonable compromise since you still get all the benefits of the Python ecosystem.

Julia seems like it has the best foundations for deep learning, since everything can be written directly in Julia. But it doesn't have a great ecosystem as a general programming tool.

F# might turn out to be a good option.

Re: Practical Deep Learning for Coders 2022

#59

Post by jph00 »

mkl wrote: post #32
jph00 wrote: post #3

Hi folks - I'm the creator/teacher of this course. I'd be happy to answer any questions that you have about the course, learning deep learning in general, or the state of deep learning in 2022.

How many hours do you think this course would take for an experienced developer with plenty of applied maths but ~no machine learning? How easy is it to do the course on my own hardware rather than cloud notebooks? Would that make it closer to practical deployment?

I'd suggesting budgeting about 80 hours for the course given that background. That should get you to a place where you can work on practical projects that are reasonably well within standard applications of deep learning.

Most practical deployment is done to cloud environments rather than local notebooks. The deployment exercise we do in the course is designed to show the key components you'll need for deploying simple models in practice.

Re: Practical Deep Learning for Coders 2022

#60

Post by jph00 »

Oreb wrote: post #38
jph00 wrote: post #3

Hi folks - I'm the creator/teacher of this course. I'd be happy to answer any questions that you have about the course, learning deep learning in general, or the state of deep learning in 2022.

Is there a new version of the book? All the links I find lead to the 2020 edition.

No, the book is continually updated for each reprint, but there isn't a separate edition.
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