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I tried to do from scratch AND hands-on in PyTorch: https://colab.research.google.com/github/stared/thinking-in-tensors-writing-in-pytorch/blob/master/3%20Line

by stared 7y ago

I tried to do from scratch AND hands-on in PyTorch: https://colab.research.google.com/github/stared/thinking-in-tensors-writing-in-pytorch/blob/master/3%20Linear%20regression.ipynb https://colab.research.google.com/github/stared/thinking-in-... By all means, much more "from scratch". It is a part of open-source "Thinking in Tensors, Writing in PyTorch": https://github.com/stared/thinking-in-tensors-writing-in-pytorch https://github.com/stared/thinking-in-tensors-writing-in-pyt...

semiotagonal 7y ago
I tried the popular Machine Learning course on Coursera, and found myself spending tons of time debugging Octave code because to me it was such an unfamiliar way of expressing things. A widely accepted alternative in a more popular programming language would be a nice thing to exist.
- graycat 7y ago
  Can see my post here https://news.ycombinator.com/item?id=21301117 https://news.ycombinator.com/item?id=21301117 I derive the normal equations in just two lines of matrix algebra, never use calculus, assume nothing about probability. The normal equations are plenty simple to program in nearly any programming language. For the linear algebra, just use LINPACK or for a really simple approach just write your own Gauss elimination linear equations solver.
- unoti 7y ago
  It does exist! There are lots of answers to what you’re asking for. But here’s the best answer: https://course.fast.ai/ https://course.fast.ai/ This course uses python and pytorch and will have you building something that can tell 30+ breeds of cats and dogs apart in the first lesson. While the old course from Andrew ng you mentioned starts with theory and by the end gets to doing something useful, the fastai course starts with best practices and moves into the underlying theory later. Also Andrew Ng has a newer deeplaearning.ai course on coursera that is all python and doesn’t do Octave.
  - semiotagonal 7y ago
    Thanks.
- curiosity_100 7y ago
  You can find detailed python solutions for the Coursera course homework online, for example: https://github.com/suraggupta/coursera-machine-learning-solutions-python https://github.com/suraggupta/coursera-machine-learning-solu...
zwaps 7y ago
I hate myself in advance for a reply like this, but I just scrolled randomly towards the loss function part of that document and it reads "You can see also expected value notation, were E[x] means an average of x, that is ∑N−1i=0xi/N." I think this is not what E[x] means, i.e. going from expectation to the average (using the sum over n-1 no less) involves a bit more than notation (in this case, assumptions about the DGP). Yes I also hate posts like this, but in the interest of this being a course on mathematics in DL - wanted tollet you know.
- stared 7y ago
  Well, it this context, there are the same, and I don't see a room for confusion. (I can add that with "uniform measure" and "arithmetic average", but for people learning it it is too much, for people knowing that the context is obvious anyway.)

by stared 7y ago

semiotagonal 7y ago

I tried the popular Machine Learning course on Coursera, and found myself spending tons of time debugging Octave code because to me it was such an unfamiliar way of expressing things. A widely accepted alternative in a more popular programming language would be a nice thing to exist.

graycat 7y ago
Can see my post here https://news.ycombinator.com/item?id=21301117 https://news.ycombinator.com/item?id=21301117 I derive the normal equations in just two lines of matrix algebra, never use calculus, assume nothing about probability. The normal equations are plenty simple to program in nearly any programming language. For the linear algebra, just use LINPACK or for a really simple approach just write your own Gauss elimination linear equations solver.
unoti 7y ago
It does exist! There are lots of answers to what you’re asking for. But here’s the best answer: https://course.fast.ai/ https://course.fast.ai/ This course uses python and pytorch and will have you building something that can tell 30+ breeds of cats and dogs apart in the first lesson. While the old course from Andrew ng you mentioned starts with theory and by the end gets to doing something useful, the fastai course starts with best practices and moves into the underlying theory later. Also Andrew Ng has a newer deeplaearning.ai course on coursera that is all python and doesn’t do Octave.
- semiotagonal 7y ago
  Thanks.
curiosity_100 7y ago
You can find detailed python solutions for the Coursera course homework online, for example: https://github.com/suraggupta/coursera-machine-learning-solutions-python https://github.com/suraggupta/coursera-machine-learning-solu...

zwaps 7y ago

I hate myself in advance for a reply like this, but I just scrolled randomly towards the loss function part of that document and it reads "You can see also expected value notation, were E[x] means an average of x, that is ∑N−1i=0xi/N." I think this is not what E[x] means, i.e. going from expectation to the average (using the sum over n-1 no less) involves a bit more than notation (in this case, assumptions about the DGP). Yes I also hate posts like this, but in the interest of this being a course on mathematics in DL - wanted tollet you know.

stared 7y ago
Well, it this context, there are the same, and I don't see a room for confusion. (I can add that with "uniform measure" and "arithmetic average", but for people learning it it is too much, for people knowing that the context is obvious anyway.)