3 ms·

> “Why do we have to write the backward pass when frameworks in the real world, such as TensorFlow, compute them for you automatically?” How many more times do

> “Why do we have to write the backward pass when frameworks in the real world, such as TensorFlow, compute them for you automatically?” How many more times do you need to see the same phenomenon under different guises before you stop asking stupid questions? "Hey teach, why do I need to learn how to multiply if I can just use a calculator?"

posterboy 10y ago
Isn't it somewhat reasonable question, given the relatively recent advent of TensorFlow, compared to ML curricula? The stress is on, why don't we learn TensorFlow / Caffe / etc.
- tnecniv 10y ago
  Because frameworks come and go. The important thing are the abstract concepts At that level, they assume you are pretty smart and capable of figuring out something like an API on your own time as needed. They'd rather you know what all these funky things in these APIs are doing at a core level so that you can employ them in an effective manner.
  - posterboy 10y ago
    I could use that same abstract argument, just that with a framework you wanna see how far you can get, not how low. From the schools perspective it's all the same, just some test on your mental powers. With a framework it's hard to think outside the frame. With a low level core it's hard to do anything really. It's a matter of compromise. Noone starts writing asm to begin with, although it is interesting, e.g. nand tetris being a famous example.
  - HelloNurse 10y ago
    Letting the computer do the menial work of constructing formulas and generating code from user specifications is a fairly important framework feature and "abstract concept".
- rahrahrah 10y ago
  Are you seriously asking that? Have you seen the example of learning multiplication vs using a calculator? What would you say in that case?
  - posterboy 10y ago
    It's pretty much the same as looking at a multiplication table on a request basis. Ironically, mere exposure to data is enouhg to learn from in ML, so why not here. Although, I'm not sure about the pedagogic aspect. I'd assume calculation by heart would be learned along the way, despite sending the initial message, it wasn't needed. Maybe starting slow is important, because it's that fundamental. But in hindsight, I really was good after half the elementary training. I have a similar anecdote: I wasn't good at handwriting and always claimed I wouldn't need to. Now I don't need to, indeed, except for exams. But I actually have a hard time with caligraphy and that's a shame.
    - rahrahrah 10y ago
      > I have a similar anecdote: I wasn't good at handwriting and always claimed I wouldn't need to. Now I don't need to, indeed, except for exams. But I actually have a hard time with caligraphy and that's a shame. Your anecdote is irrelevant because there is no _understanding_ to be gained by handwriting as opposed to typing. I really can't be bothered with this conversation, sorry.
      - posterboy 10y ago
        Writing is geometric and calculation is pretty much mechanic.
- cjf101 10y ago
  There's at least two ways to go about learning ML, and I think ideally one should do both. One is to use a high level tool like TensorFlow or Azure ML Studio to experiment with what the tools can do with data. This can get you sufficient competency to use these algorithms in a practical way. The other is to learn the foundations of those algorithms so you can best understand how to tune, apply and extend them. This is the path to mastery.

posterboy 10y ago

Isn't it somewhat reasonable question, given the relatively recent advent of TensorFlow, compared to ML curricula? The stress is on, why don't we learn TensorFlow / Caffe / etc.

tnecniv 10y ago
Because frameworks come and go. The important thing are the abstract concepts At that level, they assume you are pretty smart and capable of figuring out something like an API on your own time as needed. They'd rather you know what all these funky things in these APIs are doing at a core level so that you can employ them in an effective manner.
- posterboy 10y ago
  I could use that same abstract argument, just that with a framework you wanna see how far you can get, not how low. From the schools perspective it's all the same, just some test on your mental powers. With a framework it's hard to think outside the frame. With a low level core it's hard to do anything really. It's a matter of compromise. Noone starts writing asm to begin with, although it is interesting, e.g. nand tetris being a famous example.
- HelloNurse 10y ago
  Letting the computer do the menial work of constructing formulas and generating code from user specifications is a fairly important framework feature and "abstract concept".
rahrahrah 10y ago
Are you seriously asking that? Have you seen the example of learning multiplication vs using a calculator? What would you say in that case?
- posterboy 10y ago
  It's pretty much the same as looking at a multiplication table on a request basis. Ironically, mere exposure to data is enouhg to learn from in ML, so why not here. Although, I'm not sure about the pedagogic aspect. I'd assume calculation by heart would be learned along the way, despite sending the initial message, it wasn't needed. Maybe starting slow is important, because it's that fundamental. But in hindsight, I really was good after half the elementary training. I have a similar anecdote: I wasn't good at handwriting and always claimed I wouldn't need to. Now I don't need to, indeed, except for exams. But I actually have a hard time with caligraphy and that's a shame.
  - rahrahrah 10y ago
    > I have a similar anecdote: I wasn't good at handwriting and always claimed I wouldn't need to. Now I don't need to, indeed, except for exams. But I actually have a hard time with caligraphy and that's a shame. Your anecdote is irrelevant because there is no _understanding_ to be gained by handwriting as opposed to typing. I really can't be bothered with this conversation, sorry.
    - posterboy 10y ago
      Writing is geometric and calculation is pretty much mechanic.
cjf101 10y ago
There's at least two ways to go about learning ML, and I think ideally one should do both. One is to use a high level tool like TensorFlow or Azure ML Studio to experiment with what the tools can do with data. This can get you sufficient competency to use these algorithms in a practical way. The other is to learn the foundations of those algorithms so you can best understand how to tune, apply and extend them. This is the path to mastery.