61–70 of 103 posts
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's lunch in regards to anything AI related, am I way off base?
It's very hard to judge a book like this... (based on a table of contents?) Who is the author ? Have they published anything else highly rated ? Are there good reviews from people that know what they're talking about? Are there good reviews from students that don't know anything ?
The entire pdf is available as a free download on that page. First link at the top.
https://github.com/udlbook/udlbook/releases/download/v1.16/U...
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's lunch in regards to anything AI related, am I way off base?
Earlier quoted context omitted.
This is about deep learning, of which LLMs are a subset. If you are interested in machine learning, then you should learn deep learning. It is incredibly useful for a lot of reasons. Unlike other areas of ML, the nature of deep learning is such that its parts are interoperable. You could use a transformer with a CNN if you wish. Also, deep learning enables you to do machine learning on any type of data, text, images,…
I started off being really excited to learn, but as time went on I actually lost interest in the field. The whole thing is essentially curve fitting. The ML field is essentially an art more than a science and it's all about tricks and intuitions on different ways of getting that best fit curve. From this angle the whole field got way less interesting. The field has nothing deeper or more insightful to offer beyond th…
I think there are some really cool problems, such as:
1. Is synthetic data viable for training?
2. How do you make deep learning agents that can do task planning and introspection in complex environments?
3. How do we efficiently build memory and data lookup into AI agents? And is this better/worse than making longer context windows?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's lunch in regards to anything AI related, am I way off base?
After a glance, looks like too much for one book. Probably it was compressed with the assumption that reader already knows quite a lot. In other words it's not an easy reading.
Earlier quoted context omitted.
This is about deep learning, of which LLMs are a subset. If you are interested in machine learning, then you should learn deep learning. It is incredibly useful for a lot of reasons. Unlike other areas of ML, the nature of deep learning is such that its parts are interoperable. You could use a transformer with a CNN if you wish. Also, deep learning enables you to do machine learning on any type of data, text, images,…
I started off being really excited to learn, but as time went on I actually lost interest in the field. The whole thing is essentially curve fitting. The ML field is essentially an art more than a science and it's all about tricks and intuitions on different ways of getting that best fit curve. From this angle the whole field got way less interesting. The field has nothing deeper or more insightful to offer beyond th…
I'm worried that I'm starting a journey that requires a Master's or PhD.
Both perspectives are correct. The field is bifurcating into two different skill sets: ML engineer and ML scientist (or researcher).
It'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&D mindset that is quite alien to many of them.
This is when an AI scientists become essential.
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.
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's lunch in regards to anything AI related, am I way off base?
OpenAI is for example not interested in developing small embedded neural networks that run on a sensor chip that real-time detects specific molecules in air.