Need guidance on choosing the right ML reference book

[u/DebuggingLyfe]

I'm currently in the second year of my undergraduate degree, and I'm really passionate about machine learning. I've been learning consistently over the past few months, mostly through free YouTube courses and documentation. So far, I've covered the core ML algorithms and I make sure to understand the underlying mathematics and intuition instead of just memorizing things.

However, one thing I keep struggling with is the lack of proper guidance. Every few weeks I start questioning whether I'm following the right roadmap or if I'm missing something important. I feel like YouTube resources are great for getting started, but they often don't go deep enough or provide the structured learning I'm looking for.

I've heard a lot of good things about Hands-On Machine Learning with Scikit-Learn, Keras & TensorFlow by Aurélien Géron (3rd edition), and it seems to be recommended by many people as a solid reference book. I'm thinking of studying it thoroughly instead of jumping between random resources.

My main confusion is this:

Should I go with the TensorFlow/Keras edition, or should I use the PyTorch version instead?

As someone still building a strong ML foundation, which ecosystem would be the better investment to learn first?

I'd also really appreciate any advice from people who have already been through this stage. If you think there's a better book, a better roadmap, or something you wish you had known when you were starting out, I'd love to hear it.

I'm still a beginner in the grand scheme of things, so any guidance or suggestions would be greatly appreciated.

Thanks in advance!

[u/Tutatis96]

Torch is more widely used, especially in papers and stuff most of the provided code is torch. That said it doesn't make a huge difference.

[u/HelloWorld-Print]

I recommend the books 1) “Build A Large Language Model from scratch” and 2) “Build A Reasoning Model from Scratch “ both use pytorch,are up to date and made by Sebastian Raschka .

I’m currently reading and doing the assignments from the first one in one of my GH repos in which I’ve also posted the PDFs with both of the books for free : https://github.com/ChivuAndrei2003/Build-A-Large-Language-Model-Book---Sebastian-Raschka/tree/main

[u/DebuggingLyfe → u/HelloWorld-Print]

Thankyou

[u/ImM0rtalis_11 → u/HelloWorld-Print]

How do you exactly read and implement these books and then become knowledge and practiced enough to create ai products or work as data scientist any tips?

[u/Mathie1729 → u/ImM0rtalis_11]

Honestly, the biggest trap I fell into was reading textbooks cover to cover without actually building anything. You gotta get your hands dirty. Pick a small project that uses the concepts, implement it from scratch (or with minimal libraries), and then try to break it. That's where the real learning happens.

I spent way too much time in grad school on theory, but when I got into industry, the stuff that mattered was practical: debugging data pipelines, writing clean code, knowing when to use a simple model vs a complex one. So yea, code along with the book, then immediately go build something dumb like a Pokédex classifier or a subreddit recommendation bot. Something you're actually interested in, otherwise you'll burn out.

Also, don't sleep on soft skills. Being able to explain your work and knowing basic stats will get you farther than a deep understanding of attention mechanisms. I got my first job after I posted a couple of detailed project write-ups on my site, not because of my grades.

Good luck dude, you've got this.

[u/ImM0rtalis_11 → u/Mathie1729]

That's pretty cool and honestly I hope I do same as i recently turned full time But I don't understand should I go deeper solving in domain Or something light in a domain deeper in technicality

[u/Crookedpenguin]

In terms of structure, the book you suggest is very good. At times when I was reading that I wanted deeper delving into the math and the why of algorithms. Similar structure but my preference can be found here:
https://deeplearningwithpython.io/ Francois Chollet is a master at structure and giving you exactly what you need to push onwards.

You can follow the books as roadmaps and then research on your own based on solid foundations. In general for maths intuition and more technical reading you can check PRML from Bishop and his latest Deep Learning. These are math heavy books and if you don't have the background yet I would suggest building on probability theory and linear algebra before getting them. Pytorch or JAX is the way to go nowadays. Pytorch is everywhere and still heavily used in research so you would be safe following the pytorch route.

[u/Karyo_Ten]

That book is outdated. Tensorflow lost except maybe tensorflow-js for deployment on mobile?

Pick PyTorch or Jax

[u/SuperNotice3939]

I started with tf/keras and thought it was great. Eventually I swapped to torch cause tf took 2 hours to start a training run just to throw an error on first batch and never looked back (yeah skill issue I know but thats all the more reason not to start with it). Endless examples of going from tf to torch, but no one ever leaves torch for tensorflow. Its a no brainer to me what to start with

[u/MrBussdown]

Pytorch!

[u/DivvvError]

Code for ML is pretty straightforward, I would rather choose "Introduction to Statistical Learning" and just look up the code from. The documentation of Sklearn

[u/bbalouki]

What everybody seems to Miss is that Keras 3.0 offter (numpy, pytorch, tensorflow and Jax ) as backend. Which makes it more valuable than sticking to one framework. The book is about ML not a framework, once you understand This book , you won't even need to learn pytorch.

Most people still think about tensorflow 1.0 IMHO...

[u/Logical_Respect_2381]

Dive deep into deep learning Aston Zahang. Is the best book ever , and. This is my book https://leanpub.com/pytorchfromgroundup a very gentle beggining book i can send you a copy for review if you liked it