Open source deep learning models that programmers can download and run first try
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Re: Open source deep learning models that programmers can download and run first try
#2[deleted]
Re: Open source deep learning models that programmers can download and run first try
#3I would say that this title is misleading. A lot of what is presented there needs a strong grasp of deep learning(and the other underlying concepts behind them.), without which all you'll do is load the examples on Xcode and run them.
Moreover, I would probably encourage people to read examples of Tensorflow or Caffe2 running on iOS rather than something like Forge. Forge is an interesting project but won't really help you if you don't have a clue about MPS or Deep Learning.
Re: Open source deep learning models that programmers can download and run first try
#4I would say that this title is misleading. A lot of what is presented there needs a strong grasp of deep learning(and the other underlying concepts behind them.), without which all you'll do is load the examples on Xcode and run them. Moreover, I would probably encourage people to read examples of Tensorflow or Caffe2 running on iOS rather than something like Forge. Forge is an interesting project but won't really he…
The original title, which is also the title of the repo, was much more accurate. The author mentions these are models to download and start playing with right away, not a set of repositories to help you learn deep learning.
Re: Open source deep learning models that programmers can download and run first try
#5So sad to see the divide between iOS and Android platforms.
Half of this stuff I can't run.
Re: Open source deep learning models that programmers can download and run first try
#6There isnt really any math to deep learning other than the concept of a derivative which is taught in high school calculus. The reason deep learning papers seem mathy is people take network architectures and various elementary operations on them and try to express them symbolically in latex using summations and indexing-hell. For example the easy concept of "updating all the neurons in one layer based on the neurons in the previous layer and connecting weights" is expressed as matrix-vector multiplication for not really any apparent reason other than it is technically correct and makes for slicker notation, and I guess makes it easier to use APIs that compute gradients for you. Deep learning however is broadly an experimental science, which in many ways is the opposite of math as traditionally envisioned, in which great insights follow deductively from prior great insights. If you ask a basic question like "why should use 4 layers instead of 3?" there is no answer other than "4 works better". Similarly with gradient descent versus random search in weight space. There are many problem domains where random search is as good as any known hill-climbing heuristic search (like gradient descent). Why is GD so effective when learning image classifiers expressed as stacked weight sums? Who knows.
Re: Open source deep learning models that programmers can download and run first try
#7There isnt really any math to deep learning other than the concept of a derivative which is taught in high school calculus. The reason deep learning papers seem mathy is people take network architectures and various elementary operations on them and try to express them symbolically in latex using summations and indexing-hell. For example the easy concept of "updating all the neurons in one layer based on the neurons…
As someone who got as far as diffeq in college math, and is working his way through fast.ai right now, the impression I get is of a field that's at the start of formalization. It's like they've got the basic operations like addition and subtraction, but multiplication is still on the horizon. Or like the early days of calculus when some mathematicians called it black magic.
Re: Open source deep learning models that programmers can download and run first try
#8Gosh I wish this existed for Python.
Re: Open source deep learning models that programmers can download and run first try
#9There isnt really any math to deep learning other than the concept of a derivative which is taught in high school calculus. The reason deep learning papers seem mathy is people take network architectures and various elementary operations on them and try to express them symbolically in latex using summations and indexing-hell. For example the easy concept of "updating all the neurons in one layer based on the neurons…
As someone who got as far as diffeq in college math, and is working his way through fast.ai right now, the impression I get is of a field that's at the start of formalization. It's like they've got the basic operations like addition and subtraction, but multiplication is still on the horizon. Or like the early days of calculus when some mathematicians called it black magic.
Well neural nets have been called a Dark Art in the past, though that seems to be changing now.
Re: Open source deep learning models that programmers can download and run first try
#10There isnt really any math to deep learning other than the concept of a derivative which is taught in high school calculus. The reason deep learning papers seem mathy is people take network architectures and various elementary operations on them and try to express them symbolically in latex using summations and indexing-hell. For example the easy concept of "updating all the neurons in one layer based on the neurons…
Using matrices to perform the calculations is an optimization over doing a bunch of for loops. This vectorization results in faster code within higher level languages and on certain hardware platforms (SIMD). It's nothing to do with "slicker notation", although having written gradient descent with for loops and matrix operations, the vectorized version is simpler and cleaner to read in my opinion.