Thr best option for you is- Gradient Paperspace and Colab. Both are free and managed.
Learn Machine Learning first. Do not spend time on managing infra for ML while you are learning ML. Focus on learning ML first.
You can make decent cutting edge models and SOTA classic models just with free options. I am saying this because I have done this.
I suggest that you get Colab Pro after that.
AWS burns a hole on your pocket, and you should not spend money on that now. Although, AWS SageMaker is pretty tension-free experience.
I personally use GCP. I like the tooling around it to be the most convenient.
I suggest you learn the basics first. Learn classic ML, CNNs, RNNs, LSTM, Transformers, learn the necessary Maths, and even GANs if you are inclined.
If done in the right way, it will take you a 5/6 months to 18/20 months, depending on your time commitment, your current levels of grasp on programming and Math.
Do not rush or hurry.
When you reach that point, you can think of spending serious money for Deep Learning projects.
A few months back, I have gotten into TPUs, and these are fantastic. And GCP is my only option for these. I have only used TPUs for learning and personal project and never for work. I intend to keep it that way for a while.
can you please be specific on "necessary Math"? trying to apply pareto principle and cut down amount of time needed to brush up what seemingly all of lower division math courses.
My suggestion is to learn just high school amount of Differential Calculus, Linear Algebra. That much Statistics is not needed.
And do these the right way- forget about being able to prove stuff for test, or remembering the heuristics for solving problems in tests, or being able to pick the correct option from many in test.
Just forget how you studied for test. Learn limited things- very very deeply.
Learn why and how exactly each thing works. Each and every part.
The resources for these are-
1. Mathematics for Machine Learning: Linear Algebra (Coursera, Imperial)
2. - do - : Calculus
3. Essence of Linear Algebra Playlist: 3blue1brown
4. Essence of Calculus Playlist, Ibid
5. Khan Academy Statistics Playlist for High School
Again, understand each and every part very deeply.
This much Math is enough to get started with Machine Learning.
(You will need much much, much more if you want to be a Research Engineer or an Assistant Professor doing active research.
But you can chart your own path after a while.)
Then you start doing ML.
Then you learn whatever math is needed along the way.
Never, ever load your head with a bunch of math concepts just to "prepare" yourself for studying ML. I, very highly advise against it.
So,
Learn very basic stuff, but make the concepts crystally clear -> start doing ML -> learn more math as you face the need.
Learning math is a noble and worthy goal. But do not confuse it with "learning math so that I can study ML".
Not the parent but I would say you just need a little of statistics and a little of calculus and linear algebra. If you are interested in theory then you need more.
For statistics I would recommend "All of Statistics".
The level of calculus required is to know to differentiate.
Algebra is more important. Any introductory linear algebra book would do. If you are able to multiply matrices and solve equations you can postpone a topic until necessary like for example eigenvectors or matrix factorization.
My advice is to first jump into the pool and learn swimming as needed. But learn swimming, use the concrete problems to motivate yourself.
If you want to see what the curriculum of a "Math for ML" from a top research uni looks like, you should check out the website of Math for ML course offered by Universitat Tubingen [0]. If you know those, you will be able to read the math of most papers that you will come across.
Secondly, the best way to get started with ML is to do the Andrew Ng classic on Coursera. Then move on to FastAI [1]. Fastai is a fantastic learning resource and you will learn many nice things from Jeremy Howard that will help you make your own models. But do NOT limit yourself with fastai. It's a crappy software- too many limitation, syn sugars, API anti patterns, etc.
Learn Machine Learning first. Do not spend time on managing infra for ML while you are learning ML. Focus on learning ML first.
You can make decent cutting edge models and SOTA classic models just with free options. I am saying this because I have done this.
I suggest that you get Colab Pro after that.
AWS burns a hole on your pocket, and you should not spend money on that now. Although, AWS SageMaker is pretty tension-free experience.
I personally use GCP. I like the tooling around it to be the most convenient.
I suggest you learn the basics first. Learn classic ML, CNNs, RNNs, LSTM, Transformers, learn the necessary Maths, and even GANs if you are inclined.
If done in the right way, it will take you a 5/6 months to 18/20 months, depending on your time commitment, your current levels of grasp on programming and Math.
Do not rush or hurry.
When you reach that point, you can think of spending serious money for Deep Learning projects.
A few months back, I have gotten into TPUs, and these are fantastic. And GCP is my only option for these. I have only used TPUs for learning and personal project and never for work. I intend to keep it that way for a while.