Are there any machine learning packages that implement spiking neural networks? or any other stand-alone implementations of them that could get me started to work with?
A python library named Brian ought to be useful for you.
There's also what I believe is a programing language named NEURON, but Brian is fairly easy to learn, at least for the basics. It took me a while though to figure out how to do a couple small things, since its a really high level language or whatnot.
There are several other SNN platforms these days that allows you to run classification. I have worked with NeuCube (https://kedri.aut.ac.nz/R-and-D-Systems/neucube) which is a Matlab & Java-based SNN platform.
Also, check out Akida Development Environment (ADE) from Brainchip Inc (https://brainchipinc.com/). One of the best features of ADE is that it's APIs are based on tensorflow/keras structure and also supports CNN2SNN converter to use your deep learning models in SNN domain. SNN models developed using this platform can be deployed on their neuromorphic processor Akida.
I believe there are other platforms such as PyNN and Nengo (compatibility to run models on Loihi) within the SNN domain.
Here are links for brain simulator
https://github.com/brian-team/brian2
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2605403/
http://briansimulator.org/
You can install the Nengo Loihi library for deployment not only of spiking neural networks but also neuromorphic neural networks.
here's the link to their website: https://www.nengo.ai/nengo-loihi/v1.0.0/index.html
You can find on Kaggle an implementation of the ciphar10 dataset, locally loaded, using Nengo Loihi library. Here's the link:
https://www.kaggle.com/migueltoms/neuromorphic-ciphar-10-loihi-comparison-of-results
Related
I need advice on which libraries and game engines should I use for a ml project
my goal is to create machine learning model for pruning the trees. I believe I have to create a game with generic tree model with some randomness then create reinforcement learning model and train ml model inside the game.ML model must have ability to first find the branch which must be cut and then find a path to move robotic arm near to that branch to cut it. I have experience in c++ and java but I prefer c++ , could you give me advise which library should I use for ML and which language and game engine should I use for creating game? I have a little experience in opengl. If it doesn't make any difference my prefered language is c++ but I know that I should use right tool for right job and python is leader in ML so if it will save a time and energy I have nothing against learning python.
My recommendation is to learn and use Python for your ML project. Though there is some work in R, for your future in ML, your best bet is to learn and use Python. The community is great, and there are many frameworks that can work out-of-the-box.
After a quick search, I did find a framework called robotframework, that is pretty highly starred on GitHub here: https://github.com/robotframework/robotframework. I will say though, however, that I am not personally familiar with using this framework. But it may be helpful to you.
In terms of tree-based algorithms, you might want to start exploring with XGBoost. It can be found here: https://github.com/dmlc/xgboost.
I have started using Julia.I read that it is faster than C.
So far I have seen some libraries like KNET and Flux, but both are for Deep Learning.
also there is a command "Pycall" tu use Python inside Julia.
But I am interested in Machine Learning too. So I would like to use SVM, Random Forest, KNN, XGBoost, etc but in Julia.
Is there a native library written in Julia for Machine Learning?
Thank you
A lot of algorithms are just plain available using dedicated packages. Like BayesNets.jl
For "classical machine learning" MLJ.jl which is a pure Julia Machine Learning framework, it's written by the Alan Turing Institute with very active development.
For Neural Networks Flux.jl is the way to go in Julia. Also very active, GPU-ready and allow all the exotics combinations that exist in the Julia ecosystem like DiffEqFlux.jl a package that combines Flux.jl and DifferentialEquations.jl.
Just wait for Zygote.jl a source-to-source automatic differentiation package that will be some sort of backend for Flux.jl
Of course, if you're more confident with Python ML tools you still have TensorFlow.jl and ScikitLearn.jl, but OP asked for pure Julia packages and those are just Julia wrappers of Python packages.
Have a look at this kNN implementation and this for XGboost.
There are SVM implementations, but outdated an unmaintained (search for SVM .jl). But, really, think about other algorithms for much better prediction qualities and model construction performance. Have a look at the OLS (orthogonal least squares) and OFR (orthogonal forward regression) algorithm family. You will easily find detailed algorithm descriptions, easy to code in any suitable language. However, there is currently no Julia implementation I am aware of. I found only Matlab implementations and made my own java implementation, some years ago. I have plans to port it to julia, but that has currently no priority and may last some years. Meanwhile - why not coding by yourself? You won't find any other language making it easier to code a prototype and turn it into a highly efficient production algorithm running heavy load on a CUDA enabled GPGPU.
I recommend this quite new publication, to start with: Nonlinear identification using orthogonal forward regression with nested optimal regularization
I'm new to Deep learning. I followed some course materials on internet and I found they are using deep learning frameworks like KERAS, Tensor-flow in order to build deep neural networks. Also I found in some course materials they are building deep neural networks from that scratch rather than using frameworks. So I wanna know since I am new to deep learning what is the best thing for me. Whether using frameworks to build neural networks or building them from scratch. Is there any thing that could be missing if I directly use frameworks rather that building from the scratch.
I know that they have developed frameworks like KERAS to use. But my problem is if we depend on those frameworks will we miss basic theories of those things since I am new to this field...?
Yes, its fine, else what would be the purpose of making those frameworks in the first place?
As you found out building neural networks from scratch is not easy, at least by using Keras or other frameworks, you know that these have some degree of validation that they work correctly.
The problem by using Keras with no other assumption is that you won't be able to understand the inner working of the framework. Keras is 100k+ lines of code.
Try to use EpyNN before using Keras, it was made for that. Validated against it, it works correctly and is only ~2k lines of code.
epynn.net
With this you have an all inclusive solution to master "basic theories of those things" as you mention.
Recently, we planned to build a system for image processing to extract info from images. At present we are using AWS Rekognition to do that. But, in some cases, we are not getting accurate information from AWS. So, we've planned to build our own custom one.
We've 4/5 months to do that. At least a POC version. Also, we've planned to use Tensorflow for that. We all have no prior experience about Machine Learning & Deep Learning but already have 5/6yrs of experience on Computer Programming by using different languages.
Currently, I'm studying ML from a course of Udemy & my approach to solve this problem is...
Learn Machine Learning(ML)
Learn Deep Learning(DL)
Above ML & DL maybe I'll be ready to understand the whole thing & can able to build a system for Image Processing.
In abstract what I've understood is, I've to write one Deep Learning program in Python by using Tensorflow. By using that Program I've to build a Model. Then I've to train that Model by using some training data. Then, when my Model achieves a certain level of accuracy I'll use some test data.
Now, there some places at where I've bit confused & here are my questions regarding that confusion...
I know tensorflow is a library but at some places, it's also mentioned as a system. So, is it really a library(piece of code) only & something more than that?
I got some Image Processing Python code in Tensorflow tutorial section (https://www.tensorflow.org/tutorials/image_recognition). We've tested that code & it's working exactly the way AWS Recognition service work. So, here my doubt is... can I use this Python code as it is in our production work?
After train a model with some training data does those training data get part of the whole system or Machine Learning Model extract some META info from those training data & keep with itself rather whole raw training data(in my case it'll be raw images).
Can I do all these ML+DL programmings over my Linux System? It has Pentium 4 with 8GB RAM.
Also, want to know... the approach which I've mentioned to build a solution for my problem is sufficient or I need to do something else also.
Need some guidance to clear out all these confusion.
Thanks
1 : tensor-flow is like anything else we have been worked with (like Numpy ) but only difference is we have to first defined what we want to use the use it , every thing in tensor-flow are running into a computational graph and evaluating every thing in that graph require a Session , we could call it library because it just piece of code and have interface in python , and system because of all those mechanism it uses
2 :
can I use this Python code as it is in our production work? Why not !
3:
yes you could do that with your system , but the main advantage of tensor-flow and theano , .. the tool like those is that you could run your code on GPU it a more faster way than on CPU because the GPU could handle a lot more matrix multiplication and stuff like that
4:
you know you don't have to learn all the machine learning stuff to built a image recognition system , it may be take years for you to understand whats going on there , Udemy course is very good source but you I highly recommend you to see the machine learning courses of coursera , there is to courses there about machine learning : the great Andrew NG course and Emily fox course , the first one is more theoretical than practical , but second on is more practical ,
and about the Deep learning , there is nothing fancy about Deep learning and it's just a method in machine learning , after you gain some experience in machine learning and understood some basic or you could do it right know , go to fast.ai , it has a really good course about deep learning for coder and it's also free
I hope this will help you
I am new to the field of machine learning, I am planning to use python as the programing language for implementing algorithms and Java for system architecture.
As far as I understand, machine learning is more about modeling data specific to the domain, visualize the data, and choose appropriate models & parameters. Implementing the models/algorithms is the last and relatively easy step.
Matlab seems to have everything for machine learning but it is too expensive and requires to learn a new language.
What tools other than programming language do I need in general for machine learning for enterprise projects? things like data modeling, visualization,etc
After a couple of years of trial and error, I would suggest you to go directly with python, possibly with scikit-learn or tensorflow (if you want to go hardcore :).
I also tried R in the past, and while it is a very valid language it has some limitations: It is single threaded by default, and although there are solutions for that, they are non as clean as python.
Also, python seems to be THE language for machine learning, it is easy to learn, and fast (depending on the interpreter implementation of course), also there is huuuuuuge support for it, lots of tutorials, documentation and, more important, libraries are actively develop and supported.
Finally, i recommend you to consider spyder as a good IDE for data science, I also tried Rodeo, but it does not seem as mature and stable as spyder.
Hope this helps.