https://www.tensorflow.org/
понедельник, 28 декабря 2020 г. в 11:09:48 UTC+5, meera:
> I don't know what resources you have tried already so I'm probably going
> to state the obvious.
>
> You're probably looking for some language agnostic resources on the
> subject, since having it be Go specific will narrow your search.
>
> ML like any part of computer science can be described with algorithms,
> data structures and a sprinkle of sorcery. Looking for implementations in
> Go might help, but ultimately you're going to learn from theory, so books
> are a good start.
>
> My bet would be:
> for { // ever
> - Grab a couple of books, read the important bits of each one (textbooks
> also have bloat)
> - Look for easy algorithms and implement them
> }
>
> And don't use magical packages, when magic breaks only the sorcerer can
> patch it up, and you'll be only learning to use the _package_ and not the
> actual ML
>
>
>
>
>
> On Mon, 28 Dec 2020, 00:29 Nikolay Dubina, <[email protected]> wrote:
>
>> Go does not have much traction in ML and for good reasons:
>>
>> * not a single one organization or company is backing Go ML projects
>> * with exception couple papers, no research in neither computer vision,
>> NLP, or RL is done in Go, no papers are implemented in Go. It is mostly
>> Pytorch these days.
>> * Go language does not support: multi-dimensional indexing; N-dimensional
>> arrays; operator overloading; short lambda notation — all these are loved
>> by data science and machine learning community since it makes life a lot
>> easier for them, but not in Go
>> * Go support for GPU is not good
>> * Go compiler does not support optimizations like SIMD — so even CPU
>> intense workloads are not as performant
>> * Go calls to C can be made, but "cgo is not go" and benefits of Go
>> deteriorate quickly with this approach — so a lot of ML code in C can not
>> be really efficient with Go
>> * Audio / Video / Image / Spatial data is not supported well in Go (just
>> try to run OpenCV in Go, likely it will be either IPC or cgo...)
>> * Many ML related libraries are supported by a single person or already
>> deprecated or highly unstable or experimental
>>
>> Is there way forward?
>>
>> Writing experimentation, data visualization, data wrangling, modeling,
>> training in Go is shooting yourself in the foot. I already tried this
>> myself once for porting Julia code. I would not believe any single DS or ML
>> person would use Go seriously for these purposes.
>>
>> However, there is a niche that Go may fit — tabular data (your backend
>> data model) + inference. Which means, ML model is developed and *trained*
>> in say Python/Julia/R but then ported to Go and loaded trained model
>> artifacts. I recently wrote
>> https://github.com/nikolaydubina/go-featureprocessing as a first step in
>> that direction and more work will follow up.
>>
>> Here is what ML there is in Go at the moment:
>>
>> * https://github.com/josephmisiti/awesome-machine-learning#go
>> * https://github.com/avelino/awesome-go#machine-learning
>>
>> On Monday, December 28, 2020 at 2:11:37 AM UTC+8 [email protected]
>> wrote:
>>
>>> I think you might be better off learning AI/ML using Python - to
>>> understand the concepts - most tutorials use Python/Colab as well since it
>>> is so easy.
>>>
>>> Once you understand the concepts you can use Go libraries
>>> <https://pkg.go.dev/github.com/tensorflow/tensorflow/tensorflow/go> to
>>> implement the concepts in Go.
>>>
>>> On Dec 27, 2020, at 11:55 AM, Philip Chapman <[email protected]> wrote:
>>>
>>> I am an experienced developer and fairly knowledgeable in Go, but new to
>>> AI and machine learning. I'd like to expand my skillset in that direction.
>>> I would be happy for and recommendations and advice on good material for
>>> learning AI and machine learning with Go. Most of the material out there
>>> seems to be based on python, but I rather prefer Go.
>>>
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