How Do You Implement New Knowledge in Practical Projects?

Hey OP, are you able to share more on how the ML is being applied? In most cases you don't need to spend too much time understanding the low level math for ML unless you're dealing with customizing models.

I would focus on understanding

1. How does this model work -- the intuition behind it
2. What are the assumptions this model makes for e.g. some models assume the data is normally distributed
3. What are the pros of using this model
4. What are the cons of using this model

If you understand this you should be good for most cases.

And if you do need to actually understand how the model works in depth I suggest only learning the parts you need to as you study

E.g. you are learning about Convolutional Neural Networks - CNNs

• Understand how CNNs work intuitively
• Then you want to know what a convolution is so you dive deep into the math/code
• Then you realize you need to understand what a dot product is, so then look at that
• Keep repeating till you understand everything, almost like a DFS tree search

Other things that help:

• Look at minimal code replicas of models. Every model you want will have some version of minimal version of it (even LLMs)
• Grab the code and walk through what's happening. For CNN you might look at how the image is before and after a convolution is applied. Step through the code and understand what is happening.
• Start small, work with really small data that you can reproduce by hand. For CNNs this might be an "image" that's 5x5 0/1 and you can do the math on paper on what each step should look like

Feel free to DM me on Slack (@Sai B) if you want to chat more