ml-coursera-python-assignments VS deeplearning-notes

check this out in particular. It's the week 4 homework from Ng's course, redone by someone to be in Python instead of Octave. It's got a built in grader, so you can grab the jupyter notebook, run it locally and it'll tell you when you've got the answer right. I'd recommend taking a crack at it, then when you figure out how to code it, take a look at that micrograd library and see how you could achieve something similar using an object oriented approach.

Python version of assignments which you can submit: https://github.com/dibgerge/ml-coursera-python-assignments

Basically title. I'm halfway thru the original Stanford University Machine Learning course by Andrew Ng, but instead of using the Octave/Matlab exercises, I went with a Python repo. Now, I know the new specialisation course came out and is updated with newer content, more relevant to the state of the industry today. I have the following choices:

A language is only worth learning if it is useful to know. But the only reason 99% of people would learn Octave is just to take that course lol. Besides, (a) the original course can be completed in Python using this repo, and now his new course is actually offered in Python.

FWIW there is a repo you can use to complete the first one in Python. I used it and can vouch that it works perfectly as advertised.

You can do them in python and submit them! https://github.com/dibgerge/ml-coursera-python-assignments

Andrew Ng ML-Coursera Assignments in Python

Last piece... Octave is super easy to get into. I don't personally think it's worth doing Python versions of the homework, but if you really can't stand screwing around with a new language, this repo has alternate versions of the homework to follow that will use Python instead. You can do either these or the original versions, so don't let the Octave scare you. You don't have to use it if you really don't want to, but... like I said, it's not a big deal either way, I just did it in Octave.

Specifically, I'm referring to this github repository: https://github.com/dibgerge/ml-coursera-python-assignments/blob/master/Exercise1/exercise1.ipynb. I'm currently doing Assignment 1.

If you want in-depth understanding then I would recommend you to look for Deep Learning Specialization by Andrew Ng. (Course 4). He explained the LSTM and GRU cells in detail (mathematically). You can also find it on YouTube I guess. Hope it helps.

Best place to get started https://www.coursera.org/specializations/deep-learning

I want to expand on what I learnt theory and practice to be able to complete a project, where I can download a video and run it through my model and it will be able highlight specified items, e.g people trees, cars. Will this course help me get there https://www.coursera.org/specializations/deep-learning

Another one from DeepLearning.ai and this is also the most popular course for Deep Learning and Neural Networks - https://www.coursera.org/specializations/deep-learning

This course is a part of one of the 5 courses in DL specialization: https://www.coursera.org/specializations/deep-learning. I am taking this course on Coursera where I have finished up to week 2. Now I need to practice for it, but I think I can't access assignments as its locked for paid viewers. Can someone share me the resources for practice or any alternatives you found useful?

There is another [course] (https://www.coursera.org/specializations/deep-learning) by deeplearning ai that catched my eye and in review they say its more in detail than tf in practice course.

Deep learning like others said, but I've seen some posts recommending taking an external class, like Andrew Ng's Coursera class https://www.coursera.org/specializations/deep-learning over the GT one. I haven't taken or plan on taking the GT one but some people found it lacking

My recommendations are worth little, I'm just starting through all this stuff myself. I'm currently taking the Deep Learning specialization on Coursera and trying to map out what else I should be doing.