Understanding Deep Learning

Most comments here are in one of two camps: 1) you don&#x27;t need to know any of this stuff, you can make AI systems without this knowledge, or 2) you need this foundational knowledge to really understand what&#x27;s going on.<p>Both perspectives are correct. The field is bifurcating into two different skill sets: ML engineer and ML scientist (or researcher).<p>It&#x27;s great to have both types on a team. The scientists will be too slow; the engineers will bound ahead trying out various APIs and open-source models. But when they hit a roadblock or need to adapt an algorithm many engineers will stumble. They need an R&amp;D mindset that is quite alien to many of them.<p>This is when an AI scientists become essential.

As someone who missed the boat on this, is learning about this just for historical purposes now, or is there still relevance to future employment? I just imagine the OpenAI eats everyone&#x27;s lunch in regards to anything AI related, am I way off base?

This book looks impressive. There&#x27;s a chapter on the unreasonable effectiveness of Deep Learning which I love. Any other books I should be on the lookout for?

If I start now and start reading up on AI, will I become anything close to an expert?<p>I&#x27;m worried that I&#x27;m starting a journey that requires a Master&#x27;s or PhD.

I spent a decade working on various machine learning platforms at well known tech companies. Everything I ever worked on became obsolete pretty fast. From the ML algorithm to the compute platform, all of it was very transitory. That coupled with the fact that a few elite companies are responsible for all ML innovation, its oxymoronic to me to even learn a lot of this material.

It&#x27;s very hard to judge a book like this... (based on a table of contents?)<p>Who is the author ?<p>Have they published anything else highly rated ?<p>Are there good reviews from people that know what they&#x27;re talking about?<p>Are there good reviews from students that don&#x27;t know anything ?

Hopefully not a dumb question: how do I buy a physical copy?

The PDF figures for &#x27;Why does deep learning work&#x27; seem to point to &#x27;Deep learning and ethics&#x27; and vice versa.

No chapter on RNNs, but one on transformers is interesting, having last read Deep learning by ian goodfellow in 2016

Simply great work and making it freely available is outstanding!!

Reading through it, and it def looks accessible.

lit

All machine learning is Hopf convolution, analogous to renormalization. This should come as no surprise, renormalization can be modeled via the Ising model which itself is closely related to Hopfield networks which are recurrent networks.