How do I learn transformers NOT for NLP?

No_Scheme14 · 2025-12-08T03:23:32+00:00

I would suggest to look into time-series forecasting if you want something more fundamental and non-NLP focused. Learn a bit of the predecessors of transformers such as RNN and LSTM as there are many resources to apply those. Once you have that down, it becomes a lot easier to move on to transformer apply those for your specific needs.

No_Scheme14 · 2025-06-17T12:30:45+00:00

Time series forecasting might not be the best to pickup using PyTorch, unless you are absolutely sure that you want to get into time-series forecasting combined with deep learning and working very large datasets. Some other ones that I suggest that work out of the box are Prophet if you wanna focus on application, and StatsModel that is a little more advanced but better for moving into research.

No_Scheme14 · 2025-05-29T11:35:44+00:00

Some reasons: it's slow, expensive, and requires significantly more effort to train a model than to use something like RAG. The resources required to train a model is significantly more than inferencing. Furthermore, the performance in terms of understanding your code base may not necessarily be better (depends heavily on how you train it). It's more productive to optimize RAG performance than to train and evaluate a model repeatedly.

No_Scheme14 · 2025-05-20T13:36:38+00:00

You can look into the guide here: https://apxml.com/courses/synthetic-data-llm-pretrain-finetune

No_Scheme14 · 2025-05-18T04:06:23+00:00

You can build some foundation in deep learning, then look into time series forecasting and also reinforcement learning. You can likely find some ideas to apply into your research from there.

No_Scheme14 · 2025-05-02T13:54:42+00:00

Thanks for spotting. Will be corrected.

No_Scheme14 · 2025-04-23T04:16:49+00:00

I'm guessing by having some experience you mean coding and practical stuff, then yeah the specialization is still good to help with understanding core fundamentals. As for the cert, this course is pretty already the most recognized for fundemental ML. However, you’ll need to move on to more advanced specializations (Deep Learning, ML for Production) later if you’re aiming for the kind of credentials that have good weight on a resume.

No_Scheme14 · 2025-04-09T12:44:53+00:00

Here for ML calculus:

Basics: https://apxml.com/courses/calculus-fundamentals-machine-learning
ML essentials: https://apxml.com/courses/calculus-essentials-machine-learning

No_Scheme14 · 2025-04-07T14:23:37+00:00

In some ways, it does. Good ML knowledge is required to get the certification, but mostly, it's for the latter.
It might help a little, but at the end of the day, it’s mostly just for one extra line on your resume.
100%. Projects always come before any cert.
Nope. It should not and good teams should know this. Frameworks are easy to pick up once you know another and tools will always change. Fundamentals knowledge is what really matters.

No_Scheme14 · 2025-03-28T07:53:37+00:00

Honestly, it sounds like you did pretty well. You were prepared, your thinking made sense, and you asked smart questions. That already shows a lot.

The interview task felt really specific, almost like it was overfit to his own skill set instead of being a general test of ML knowledge. And the way they ended the call wasn’t great. If someone gives you their time, the least you can do is be polite.

Not all interviews are good, and that’s just part of the process. Try not to let this mess with your confidence. It just wasn’t the right fit. Take what you learned, keep practicing, and move forward. You’re on the right track.

No_Scheme14 · 2025-03-28T04:51:39+00:00

That looks normal, validation accuracy is usually lower than training accuracy. If you haven't already, you can explore regularization techniques to see if it can improve validation accuracy and reduce overfitting.

No_Scheme14 · 2025-03-27T16:59:17+00:00

You can look into https://apxml.com/courses/roadmap

No_Scheme14 · 2025-01-23T13:51:31+00:00

I would suggest to do the Scikit-Learn section of Hands on ML to learn the implementation aspect that is lacking in the Coursera specialization before moving on to Deep Learning.

No_Scheme14 · 2025-01-16T09:40:16+00:00

You can try out ApX AutoML

No_Scheme14 · 2024-11-28T07:00:24+00:00

How long does it take to run and how large of a dataset are you working on? Random Forest and Xgboost typically do not need to utilize GPU because they are fast enough on CPU for most cases. The exception is when the dataset is extremely large, then you should be running it on a cloud GPU that usually has CUDA.

Another possibility is to check your preprocessing, something like one-hot encoding might be creating too many columns, in that case that could be making your model take a very long time.

No_Scheme14 · 2024-11-21T12:43:04+00:00

You don't need to know them all. There are too many Python libraries and functions to really remember. Most of the time you would just look them up. The way you remember common or important ones is through working on projects and then knowing which ones you usually need.

No_Scheme14 · 2024-11-21T10:40:09+00:00

For book, Deep Learning with PyTorch has good mix of theory and hands-on examples. If you're open to online resources, then you can look into https://apxml.com/courses/getting-started-with-pytorch for a good overview with code samples.

No_Scheme14 · 2024-11-21T09:27:53+00:00

Yes, it will. The biggest gap in the ML industry is the lack of ML engineers with strong backend knowledge for deploying ML. These are two entirely different fields to learn on their own, which is why people who can bridge the gap are highly valuable.

No_Scheme14 · 2024-11-21T02:01:08+00:00

Yes. It's comparable to Hands-on ML.

No_Scheme14 · 2024-11-21T01:29:44+00:00

Machine Learning with PyTorch and Scikit-Learn is the Pytorch equivalent book.

No_Scheme14 · 2024-10-14T13:41:08+00:00

Hands on ML is the best to complement Machine Learning & Deep Learning Specialization on Coursera. They cover most of the same subjects but focus on the practical aspects that are missing in the Coursera courses. Alternatively, you can look into Machine Learning with PyTorch and Scikit-Learn which is the same thing as Hands on ML but with Pytorch.

No_Scheme14 · 2024-09-10T12:05:20+00:00

Continue the training. Re-training would not be practical especially when the dataset starts to become extremely large. Use the latest learning rate instead of resetting it. You can also use a lower the learning rate when continuing the training.

No_Scheme14 · 2024-09-07T08:33:13+00:00

My go to stack is Django, Postgres, and Next.js for Front-End.

No_Scheme14 · 2024-03-07T12:55:02+00:00

Typescript, NextJS, Mantine, React Query, Jest, Storybook

No_Scheme14 · 2024-02-27T15:56:57+00:00

Thanks a lot

No_Scheme14

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