NNfSiX VS deeplearning-notes

I've been rather enjoying "Neural Networks from Scratch" (https://nnfs.io/)

I haven't gotten super deep into it yet, but https://nnfs.io/ has been good in my opinion. The book slowly replaces written and explained code with numpy equivalents to keep the examples fast. Plus the accompanying animations are also useful. I would be curious what others think on it too.

For those curious to writing "gradient descent with respect to some loss function" starting from an empty .py file (and a numpy import, sure), can't recommend enough Harrison "sentdex" Kinsley's videos/book Neural Networks from Scratch in Python [1].

[1] https://youtu.be/Wo5dMEP_BbI?list=PLQVvvaa0QuDcjD5BAw2DxE6OF... https://nnfs.io

Not sure if foundational (quite a tall order in such a fast-moving field), but for sure a nice introduction into neural networks, and even mathematics in general (because it's nice to see numbers in action beyond school-level algebra):

Harrison Kinsley, Daniel Kukiela, Neural Networks from Scratch, https://nnfs.io, https://www.youtube.com/watch?v=Wo5dMEP_BbI&list=PLQVvvaa0Qu...

Have you had a look at https://nnfs.io/ ? I bought the book and am gearing up to start working through it, I would be interested to know your thoughts. Generally I want to chart a personal curriculum from data engineer to practical application of modern AI to real business problems.

You can check out Neural Networks from Scratch in Python for an introduction to neural networks, which can be used for image classification. Please be forewarned that you'll need the mathematics necessary to read through this book - however, I'm assuming that since you've selected writing such an algorithm(s) in Python for your final school project that you're aware of such.

I am planning on working my way through Neural Networks From Scratch (https://nnfs.io/) in a few months just to build my understanding. After that I'm hoping to be able to figure out the best path for a couple of projects I have in mind.

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.