6 ms·

If I was going to try machine learning I would code up from scratch rather than relying on black boxes.

mks40 10y ago
I feel at this point this is not a sensible thing to do any more unfortunately. I totally get the impulse though. For instance, there is still nothing great on the JVM for deep learning with symbolic differentiation (deeplearning4j does not have this, correct me if this has changed). On the other hand, I realize that between writing native interfaces, symbolic differentiation (e.g. writing a port of autograd), network optimisers, custom layers, parameter servers, multi-GPU scheduling and so forth, I'd spend years before getting to do what I wanted to implement in the first place.
- agibsonccc 10y ago
  We are adding this now to our tensor library: https://github.com/deeplearning4j/nd4j/pull/1750 https://github.com/deeplearning4j/nd4j/pull/1750 I've also added numpy interop via our new python interface jumpy: https://github.com/deeplearning4j/jumpy https://github.com/deeplearning4j/jumpy We are doing a lot more than autograd though, this is going to support dynamic computation graphs, give you direct access to a graph data structure and will later be usable from nd4s (our scala wrapper) Rather than spending time going back and forth implementing all of those things you could just pitch in with our existing efforts (hint: you'd actually be getting something done rather than debating ;)) We have a parameter server for word2vec, various kinds of optimizers and the like:https://github.com/deeplearning4j/nd4j/tree/master/nd4j-parameter-server-parent https://github.com/deeplearning4j/nd4j/tree/master/nd4j-para... I'd also just like to note for anyone else reading this: Mulling over doing something helps no one. If you see something that's open source that's close to what you want try engaging the authors to see what they have to say. Maybe they will guide you. We've done that recently for our lapack integration with cpu and gpu as well as various neural net implementations. No offense but it kills me to see comments like this. I see tons of people complaining about features yet doing nothing to add them let alone engaging open source authors. It's kinda funny - every time someone has actually did that I've hired them. The developers that actually take action when engaging open source are amazing people.I have a feeling it's because they take the time to learn and get their feet wet even if it's initimidating. Other neat community initiatives include flink: https://issues.apache.org/jira/browse/FLINK-5782 https://issues.apache.org/jira/browse/FLINK-5782 Nasa (Apache Tika): https://github.com/apache/tika/pull/165 https://github.com/apache/tika/pull/165 A language for our ETL library DataVec (supports binary vectorization AND sql like transformations!): https://github.com/deeplearning4j/DataVec/issues/224 https://github.com/deeplearning4j/DataVec/issues/224 A scala lib like tensorflow built on top of nd4j: https://github.com/ThoughtWorksInc/DeepLearning.scala https://github.com/ThoughtWorksInc/DeepLearning.scala Our spark ml integration: https://github.com/deeplearning4j/deeplearning4j/tree/master/deeplearning4j-scaleout/spark/dl4j-spark-ml https://github.com/deeplearning4j/deeplearning4j/tree/master... The community is very active. We have 4200 people in a gitter room alone: http://gitter.im/deeplearning4j/deeplearning4j http://gitter.im/deeplearning4j/deeplearning4j
  - mks40 10y ago
    I have contributed to dl4j though ;) I did not mean not criticise dl4j at all, I was simply pointing out an example of a feature I know I was missing at a point, I think we are actually agreeing. It does not always make sense to start something from scratch even though it's fun and a great learning experience. The ramp-up to something really useful in deep learning is simply very high. Further, few people can be an expert on the whole stack and I have no problem admitting to myself that even if I spent 2 years writing something from scratch, many parts would simply not be as good as something I could copy from an existing open source library. That's why contributing to open source also makes more sense to me - you get to work on a part that you can be good at. Also should point out that when I was having problems with custom loss functions a year ago you guys were extremely helpful on Gitter in discussing issues.
    - agibsonccc 10y ago
      Hard to tell from an HN user name :D. That's great to hear! I get how hard it can be - what you get out of it is learning though. We have some seriously cool examples that are just weekend projects for folks right now with javafx for example: https://github.com/deeplearning4j/dl4j-examples/pull/421 https://github.com/deeplearning4j/dl4j-examples/pull/421 A lot of community contributions are in the examples now, if you haven't used dl4j in a while maybe take a look.
  - RandyRanderson 10y ago
    You need to have some demos that one can download and get to work easily. Before you say "we do - look at this link..." see sentence 1.
    - agibsonccc 10y ago
      https://deeplearning4j.org/quickstart https://deeplearning4j.org/quickstart I agree with you if my aim were to mainly promote new users here - I was more targeting someone who knew what dl4j was already and had maybe used it. I usually don't comment unless someone mentions the library by name. 99.99999% of the people who comment on here are going to likely be more interested in python in which I usually point them at keras. Thanks for the feedback though! I'm not sure what to do beyond "git clone and import into intellij". If you'd like feel free to file an issue on I'm guessing? the nd4j repo you were looking at? We always take feedback seriously if people take 5 seconds to post problems they've found. My head of training does our docs and videos and updates the site when he can. Here are some of our youtube videos: https://www.youtube.com/channel/UCa-HKBJwkfzs4AgZtdUuBXQ https://www.youtube.com/channel/UCa-HKBJwkfzs4AgZtdUuBXQ
Toenex 10y ago
Probably a better thing to do would be to download one of the many excellent open source libraries and explore how they work. You can then even contribute back. Many open source projects would really benefit from new users contributing to documentation for a start.
- MeghdeepRay 10y ago
  Could you name a few such open source projects ?
  - dagw 10y ago
    scikit-learn is a production ready library that has some very well commented and easy to read source code. https://github.com/eriklindernoren/ML-From-Scratch https://github.com/eriklindernoren/ML-From-Scratch is an easy to understand understand implementation of some the basic ML algorithms built from first principles and aims for readability over performance.
    - MeghdeepRay 10y ago
      I was asking about the ones which don't have very good documentation and are in need of contributors.
      - nl 10y ago
        I find that the XGBoost documentation is pretty lacking.
gabrielgoh 10y ago
to code a machine learning library from scratch, you must first invent the universe
pretzelboo 10y ago
I guess, it purely depends on what you hope to achieve. If you're going to spend a few months learning how ML works, sure you'll benefit immensely. But if you're planning to apply in some field/area, writing your own library and making sure it's better than others, well, it's not going to be easy or fast..
aub3bhat 10y ago
Please don't spread bad advice. There is a reason we teach Python and not ia64 Assembly to beginners.
- Safety1stClyde 10y ago
  > There is a reason we teach Python and not ia64 Assembly to beginners. Fashion is a reason, but it's not a good reason.
  - taurath 10y ago
    Relevance and accessibility are others.
  - unpopulatedopin 10y ago
    we kinda do both, most basic functions and ops have direct complements in high and low level langs. Actually, a good course starts with logic, binary, and some electronics on the side.
muktabh 10y ago
Here is something you should check out in that case https://github.com/eriklindernoren/ML-From-Scratch https://github.com/eriklindernoren/ML-From-Scratch . Although be warned, everything from scratch approach l, despite being addictive, is harder than standing on shoulders of giants and takes too much resilience. If you are even a bit of procrastinater like me, it's tend to stay a sweet fantasy a lot of times. It's only when I was half working on a project and had no other option, I could go for implementing my first algorithm from scratch.
- deleted 10y ago
  [deleted]
dagw 10y ago
Why not take a well tested open source machine learning library and read the source code. No black box, and no spending a lot of time having re-inventing the wheel just to get some work done.
- nerdponx 10y ago
  Not just the code. The code is rarely enlightening. To actually understand what's happening you'll need to at least skim the research behind it.
agibsonccc 10y ago
As someone who actually did this, got in to YC, raised a few million and built a team of 22 distributed across 6 timezones around said endeavor: Don't. It's been 3.5 years since I did it and I don't regret it necessarily but the only reason I did was because I wanted something in an unserved category.
- Safety1stClyde 10y ago
  If I was going to try machine learning, I would code it myself from scratch rather than rely on black boxes.
  - jacquesm 10y ago
    You wouldn't even get to first base without re-inventing all those wheels, in the meantime your first competitor leveraging open source available libraries and models would plow you into the ground. If you're running a business time to market matters. If you're a hobbyist you can afford to re-invent a wheel to just as a learning experience. Don't ever mix the two, especially not if you're going to ask other people (friends, family, outsiders) to invest.
    - agibsonccc 10y ago
      I agree with this quite a bit. I mentioned the fact it took me building a business around it to justify effort in to it. Doing it "right" requires a lot of time and a team.
itchyjunk 10y ago
If you wish to make an apple pie from scratch, you must first invent the universe. - Carl Sagan
- Safety1stClyde 10y ago
  How many roads must a man walk down? Forty-two - The mice.
nerdponx 10y ago
These algorithms usually are not black boxes with respect to their mathematical content. And the code is usually open source, so you are free to audit them for your arrogant self. The term "black box" is used to refer to the fact that it's not always clear how to interpret a model like a deep neural network. It's a statistical issue, not a software issue.

mks40 10y ago

I feel at this point this is not a sensible thing to do any more unfortunately. I totally get the impulse though. For instance, there is still nothing great on the JVM for deep learning with symbolic differentiation (deeplearning4j does not have this, correct me if this has changed). On the other hand, I realize that between writing native interfaces, symbolic differentiation (e.g. writing a port of autograd), network optimisers, custom layers, parameter servers, multi-GPU scheduling and so forth, I'd spend years before getting to do what I wanted to implement in the first place.

agibsonccc 10y ago
We are adding this now to our tensor library: https://github.com/deeplearning4j/nd4j/pull/1750 https://github.com/deeplearning4j/nd4j/pull/1750 I've also added numpy interop via our new python interface jumpy: https://github.com/deeplearning4j/jumpy https://github.com/deeplearning4j/jumpy We are doing a lot more than autograd though, this is going to support dynamic computation graphs, give you direct access to a graph data structure and will later be usable from nd4s (our scala wrapper) Rather than spending time going back and forth implementing all of those things you could just pitch in with our existing efforts (hint: you'd actually be getting something done rather than debating ;)) We have a parameter server for word2vec, various kinds of optimizers and the like:https://github.com/deeplearning4j/nd4j/tree/master/nd4j-parameter-server-parent https://github.com/deeplearning4j/nd4j/tree/master/nd4j-para... I'd also just like to note for anyone else reading this: Mulling over doing something helps no one. If you see something that's open source that's close to what you want try engaging the authors to see what they have to say. Maybe they will guide you. We've done that recently for our lapack integration with cpu and gpu as well as various neural net implementations. No offense but it kills me to see comments like this. I see tons of people complaining about features yet doing nothing to add them let alone engaging open source authors. It's kinda funny - every time someone has actually did that I've hired them. The developers that actually take action when engaging open source are amazing people.I have a feeling it's because they take the time to learn and get their feet wet even if it's initimidating. Other neat community initiatives include flink: https://issues.apache.org/jira/browse/FLINK-5782 https://issues.apache.org/jira/browse/FLINK-5782 Nasa (Apache Tika): https://github.com/apache/tika/pull/165 https://github.com/apache/tika/pull/165 A language for our ETL library DataVec (supports binary vectorization AND sql like transformations!): https://github.com/deeplearning4j/DataVec/issues/224 https://github.com/deeplearning4j/DataVec/issues/224 A scala lib like tensorflow built on top of nd4j: https://github.com/ThoughtWorksInc/DeepLearning.scala https://github.com/ThoughtWorksInc/DeepLearning.scala Our spark ml integration: https://github.com/deeplearning4j/deeplearning4j/tree/master/deeplearning4j-scaleout/spark/dl4j-spark-ml https://github.com/deeplearning4j/deeplearning4j/tree/master... The community is very active. We have 4200 people in a gitter room alone: http://gitter.im/deeplearning4j/deeplearning4j http://gitter.im/deeplearning4j/deeplearning4j
- mks40 10y ago
  I have contributed to dl4j though ;) I did not mean not criticise dl4j at all, I was simply pointing out an example of a feature I know I was missing at a point, I think we are actually agreeing. It does not always make sense to start something from scratch even though it's fun and a great learning experience. The ramp-up to something really useful in deep learning is simply very high. Further, few people can be an expert on the whole stack and I have no problem admitting to myself that even if I spent 2 years writing something from scratch, many parts would simply not be as good as something I could copy from an existing open source library. That's why contributing to open source also makes more sense to me - you get to work on a part that you can be good at. Also should point out that when I was having problems with custom loss functions a year ago you guys were extremely helpful on Gitter in discussing issues.
  - agibsonccc 10y ago
    Hard to tell from an HN user name :D. That's great to hear! I get how hard it can be - what you get out of it is learning though. We have some seriously cool examples that are just weekend projects for folks right now with javafx for example: https://github.com/deeplearning4j/dl4j-examples/pull/421 https://github.com/deeplearning4j/dl4j-examples/pull/421 A lot of community contributions are in the examples now, if you haven't used dl4j in a while maybe take a look.
- RandyRanderson 10y ago
  You need to have some demos that one can download and get to work easily. Before you say "we do - look at this link..." see sentence 1.
  - agibsonccc 10y ago
    https://deeplearning4j.org/quickstart https://deeplearning4j.org/quickstart I agree with you if my aim were to mainly promote new users here - I was more targeting someone who knew what dl4j was already and had maybe used it. I usually don't comment unless someone mentions the library by name. 99.99999% of the people who comment on here are going to likely be more interested in python in which I usually point them at keras. Thanks for the feedback though! I'm not sure what to do beyond "git clone and import into intellij". If you'd like feel free to file an issue on I'm guessing? the nd4j repo you were looking at? We always take feedback seriously if people take 5 seconds to post problems they've found. My head of training does our docs and videos and updates the site when he can. Here are some of our youtube videos: https://www.youtube.com/channel/UCa-HKBJwkfzs4AgZtdUuBXQ https://www.youtube.com/channel/UCa-HKBJwkfzs4AgZtdUuBXQ

Toenex 10y ago

Probably a better thing to do would be to download one of the many excellent open source libraries and explore how they work. You can then even contribute back. Many open source projects would really benefit from new users contributing to documentation for a start.

MeghdeepRay 10y ago
Could you name a few such open source projects ?
- dagw 10y ago
  scikit-learn is a production ready library that has some very well commented and easy to read source code. https://github.com/eriklindernoren/ML-From-Scratch https://github.com/eriklindernoren/ML-From-Scratch is an easy to understand understand implementation of some the basic ML algorithms built from first principles and aims for readability over performance.
  - MeghdeepRay 10y ago
    I was asking about the ones which don't have very good documentation and are in need of contributors.
    - nl 10y ago
      I find that the XGBoost documentation is pretty lacking.

gabrielgoh 10y ago

to code a machine learning library from scratch, you must first invent the universe

pretzelboo 10y ago

I guess, it purely depends on what you hope to achieve. If you're going to spend a few months learning how ML works, sure you'll benefit immensely. But if you're planning to apply in some field/area, writing your own library and making sure it's better than others, well, it's not going to be easy or fast..

aub3bhat 10y ago

Please don't spread bad advice. There is a reason we teach Python and not ia64 Assembly to beginners.

Safety1stClyde 10y ago
> There is a reason we teach Python and not ia64 Assembly to beginners. Fashion is a reason, but it's not a good reason.
- taurath 10y ago
  Relevance and accessibility are others.
- unpopulatedopin 10y ago
  we kinda do both, most basic functions and ops have direct complements in high and low level langs. Actually, a good course starts with logic, binary, and some electronics on the side.

muktabh 10y ago

Here is something you should check out in that case https://github.com/eriklindernoren/ML-From-Scratch https://github.com/eriklindernoren/ML-From-Scratch . Although be warned, everything from scratch approach l, despite being addictive, is harder than standing on shoulders of giants and takes too much resilience. If you are even a bit of procrastinater like me, it's tend to stay a sweet fantasy a lot of times. It's only when I was half working on a project and had no other option, I could go for implementing my first algorithm from scratch.

deleted 10y ago
[deleted]

dagw 10y ago

Why not take a well tested open source machine learning library and read the source code. No black box, and no spending a lot of time having re-inventing the wheel just to get some work done.

nerdponx 10y ago
Not just the code. The code is rarely enlightening. To actually understand what's happening you'll need to at least skim the research behind it.

agibsonccc 10y ago

As someone who actually did this, got in to YC, raised a few million and built a team of 22 distributed across 6 timezones around said endeavor: Don't. It's been 3.5 years since I did it and I don't regret it necessarily but the only reason I did was because I wanted something in an unserved category.

Safety1stClyde 10y ago
If I was going to try machine learning, I would code it myself from scratch rather than rely on black boxes.
- jacquesm 10y ago
  You wouldn't even get to first base without re-inventing all those wheels, in the meantime your first competitor leveraging open source available libraries and models would plow you into the ground. If you're running a business time to market matters. If you're a hobbyist you can afford to re-invent a wheel to just as a learning experience. Don't ever mix the two, especially not if you're going to ask other people (friends, family, outsiders) to invest.
  - agibsonccc 10y ago
    I agree with this quite a bit. I mentioned the fact it took me building a business around it to justify effort in to it. Doing it "right" requires a lot of time and a team.

itchyjunk 10y ago

If you wish to make an apple pie from scratch, you must first invent the universe. - Carl Sagan

Safety1stClyde 10y ago
How many roads must a man walk down? Forty-two - The mice.

nerdponx 10y ago

These algorithms usually are not black boxes with respect to their mathematical content. And the code is usually open source, so you are free to audit them for your arrogant self. The term "black box" is used to refer to the fact that it's not always clear how to interpret a model like a deep neural network. It's a statistical issue, not a software issue.