A lot of Stanford classes are free on youtube. They are probably among the best. Andrew Ng coursera classes are modified from his Stanford class. Most of math are not difficult, linear algebra, caculus and some statistics like maximum likelihood etc. Math can be more difficult if you want to study some branches of deep learning. The goal is to establish approximate functions with deep learning which is stacking up basic simple units into multiple layers of a deep network.

>univeristy of Tübingen

Another interesting one. Thank you for the recommendation! I got the all the time in the world to dig through those.

Hopefully I won't dissapoint you guy. Thanks for the help! :D

I'm deciding over whether to take Andrew Ng's course first, or go to Khan Academy and get some math fundamentals first.

linear calculus? btw, that's an intersting channel :) thank you!

I'm constantly thinking about what you can with it. you can do almost everything. ChatGPT is crazy.

So much so stuff to learn, so much stuff to do lol. I wish I haven't wasted my early 20's playing games...

Thank you btw :)

I'm readdy to do whatever it takes :D I cannot get it out of my head lately. It's very exciting.

What maths concepts should I learn about aside from calculus?

Thank you :)

I heard good things about Andrew Ng's course. I will definitely check it out!

I started with FastAI because he said they'd go over the maths in the course and like I said it was something I was scared of. There's Khan Academy that has free courses for maths, which is pretty cool :D

I want to get good at it, not just copy paste things and hope it'll works.

The univeristy of Tübingen has really great courses for DL/ Math for DL (recently the essentials for Math have been posted) on YouTube. It convers the theory in much more depth than other courses I've seen online.

what do you think of msi suprim or gigabyte waterforce performance/quality? both of these are 2 slots gpu with liquid cooling

There's no way that you can understand DL unless you're proficient with some basic calculus (matrix multiplications, rank of a matrix, norms etc). You don't need to be good at math, but you really need to understand some concepts.

If you don't understand math, you won't improve, you will be just "imitating" people who know those concepts. You will be able to delay your complete failure, anyway.

The Coursera Deep Learning specialization is great. It starts with the basics, including a gentle introduction to the intuition behind the maths, then goes on to cover many important application areas. If you like a more structured approach (e.g. assignments, quizzes), then this is for you. It's quite a lot of work, but it will get you from completely clueless to comfortable with most of the concepts and ready to explore the field on your own.

I found the FastAI course too light on details and the Jupyter Notebook based deep learning framework they built abstracts too many details away ...and is yet another (not very popular / used in practice) framework to learn.

Facial attribute analysis, that is identifying the gender (one of the attributes) of the face detected. That's why face detection, the preliminary step, has to be done in a way that only human faces are detected.

It depends on how in depth you would like to go. The intro to PyTorch on udacity is free and a great tool to learn how to start coding deep learning projects.

For a deeper understanding it is probably best to start at the basics of linear calculus (I’d recommend three blue one brown on YouTube), and understanding what happens to vectors visually when a transformation is applied.

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I say, start with the FastAi course, it's great. Start somewhere, there isn't a defined roadmap for deep learning. Everybody has a different journey of learning these things. Also, start exploring the domains of deep learning problems, like computer vision and natural language processing. Find out what interests you the most and learn more about it.

This. We have no context of what ML even entails here. It’s too broad.

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I see a lot of people mentioning needing a GPU for DL, but it appears no one has yet clarified you only need that for training.

If you're looking for the standard use case of training a model, saving it, and then productionizing that model by exposing an API for model inference only, then you only need a GPU for the training phase. For inference, you do not need a GPU. AWS rents specialized EC2 instances with fast CPUs optimized specifically for model inference.

Another major difference may be that business requirements may preclude the use of Deep Learning in the solution. For instance, business areas like credit risk are regulated and require a level of model explainability that we can't provide with neural networks.

Others have already made great comments regarding tabular vs unstructured data, no other comments to add there.

One final thing area is the sheer volume of data needed for a DL solution vs a "Shallow" ML solution. You need orders of magnitude more data to successfully train most DL models than you do to get good performance with most other ML algorithms.

Its not a single model. But yeah that's a good starting point. There is also Entailment Writer.

Is the model called RuleTakers?

For reasoning go through allen ai's work. They are the best. They don't view it as an NLP problem, they view transformers as an agent which can do many things.

Sometimes tqdm goes nuts.

Sorry for being blunt, wtf is productization in this context, what does this word include? This is way too broad of a question, there are many nuances in ml/dl development, too many varibles could change based on a specific use case.

Simple models can be used just with the trained model and some API calls, this is the same between DL and ML. Non computational intensive tasks don’t even need GPUs/TPUs, most can even run on embedded hardwares. However they differ in amount of data required for training; data formats/ types also matter, typical ml algorithms work better with tabular data, but you wouldn’t use them for images. I mean what kind of garbage question is this lol. You can write a whole book on this.

If I get asked this question I’d ask back for a more concrete example, throwing out a generalized question only indicate the interviewer does not have the know how in ml/dl operations.

You’re still not asking a clear question. Using ML to build a product or a model being the product. If the model is the product, then your answer is “What’s the difference between an non-DL ML model and a DL model”.

Then it's strange. Unless you're using a similarly sized student model, there is no reason why a no_grad teacher and a student are similarly resource intensive as a teacher with backprop.

As a rule of the thumb, you should expend several times less memory. How much less are you expending for the same batch size in your case?

The question is about productization