Recent comments in /f/deeplearning

hayAbhay t1_j2m7l6n wrote

Reply to Any inspiring and engaging deep learning courses for beginners? by westeast1000

If you're a complete beginner and you're okay with a specific domain, I highly recommend the UMich Deep learning for Computer Vision by Justin Johnson.This is an excellent introductory course since it assumes no prior knowledge but most importantly Justin does an excellent job at providing solid foundational intuitions for deep learning (he taught CS231 with Karpathy). If you don't like Computer Vision, I still recommend the first 6-7 lectures.

I'll always recommend Andrew Ng's course for some broad basics alongside it as well. After that, you can jump into NYU's DL course by Yann and Alfredo. Imo Yann provides some of the best and most concise abstractions for some very complex concepts. If you're a beginner, some of it might go over your head. But once you have some general sense for the lay of the land and hands on experience, his abstractions are profound.

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psychorameses t1_j2e63yf wrote

Reply to comment by Ashraf_mahdy in Incorporating Deep Learning into my MSc by Ashraf_mahdy

That distinction isn't real. In that regard, both techniques learn exactly the same thing: a best fit curve. The difference is how complicated that curve needs to be. Unless you are trying to do something specific like computer vision or natural language processing, you really don’t need DL. If you are working with simple tabular data, basic ML like linear regression will be more than enough.

In any case, the feedback for most ML projects is to start with a simple regression technique and only start complicating your models if you aren’t getting what you want. You’d be surprised to see how far a simple non-DL model gets you.

I worked in Zillow’s AI team so I know both AI and real estate analytics problems.

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Ashraf_mahdy OP t1_j2cxhvw wrote

Reply to comment by ragdoll438 in Incorporating Deep Learning into my MSc by Ashraf_mahdy

I use the term DL/ML/AI interchangeably so maybe I am mistaken in this regard.
However, I did research the difference between Statistical learning and ML and the idea is that statistical learning is about relations between variables whereas DL/ML is about learning from a "random" so to speak dataset. In my case one time events can affect the statistical learning outcomes, however I am planning a "fall-back" method of statistical learning as well if that makes sense

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ragdoll438 t1_j2bursi wrote

Reply to Incorporating Deep Learning into my MSc by Ashraf_mahdy

Why do you think your thesis has to involve deep learning? you should first focus on problem and not technic. DL is useful when you have millions of data points and in most of the use cases classical ML/statistical learning is enough

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ralphc t1_j2a17xo wrote

Reply to Laptop for Machine Learning by sifarsafar

What's your budget?

I have a Dell Alienware 15" laptop with a rtx 3080 Ti, 16 GB of GPU memory, that does well on deep learning, tensorflow etc.

With Windows 11 on it you can set up WSL 2 and run graphical Linux programs. CUDA has a WSL-specific setup to get to the GPU and the rest is easy to set up.

It looks like you can get one in the $2500-3000 range, that's why I asked about your budget.

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ShadowStormDrift t1_j29wqlt wrote

Reply to Laptop for Machine Learning by sifarsafar

I have a Mac M1 Pro. Given to me by my work.

DO NOT. I REPEAT. DO NOT USE A MAC TO DO DEEP LEARNING.

You will not have a good time.

Their decision to go with their own architecture (One chip as CPU and GPU) has completely gimped them in this space.

Most popular DL frameworks ship with CUDA. Cuda is controlled by Nvidia. Native M1 chips are not compatible with CUDA.

This means by doing DL on a Mac you are locking yourself out of the entire DL ecosystem.

Additionally, they (Apple) are also highly restrictive upon what they do and do not allow on their eco system leading to a VERY restrictive development environment. Seriously, getting something like OpenRefine working on a Mac was not possible due to their stance of "Only authorized programs may be installed here". At the time of my attempt, OpenRefine, a highly popular framework for inspecting massive CSV files, was not authorized on the new Mac M1 series.

Sure they may eventually deign to authorize something as popular as OpenRefine... but frankly you will be better off getting actual work done instead of waiting for a company to realize that nobody is big enough to police the entirety of the internet.

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