I started by implementing some simple threshold neurons. The current goal is a multilayer perceptron (similar to the one in scikit-learn), and maybe other kinds of networks, such as self-organizing maps or radial basis networks. I could try to implement a deep learning algorithm, but the big issue with them is time complexity. Probably, it would require the use of GPU, or some advanced "tricks", so I should start with something smaller. Also, I want to try different kinds of design approaches, including those that are not based on highly optimized vector algebra (I know that it might not be the best idea, but I want try it and see what happens). For example, a network, where each neuron is an object (normally the whole network is represented as a collection of weight matrices). It might turn out to be very slow, but more object-friendly. For now it's just an idea, but to try something like that I would need a small network with 1-100 neurons. Yours sincerely, Oleksandr
Hi Oleksandr, I'm working half-time on a team doing simulations of spiking neural networks (on scala). Since the topic is "new" to me, I started following a MOOC on traditional machine learning and putting some of my code in here: https://github.com/guillep/neural-experiments Also, I wanted to experiment on hand-written characters with the Mnist dataset (yann.lecun.com/exdb/mnist/) so I wrote a reader for the IDX format to load the dataset. https://github.com/guillep/idx-reader If you want I can take a look and we can discuss further :) Guille On Tue, Mar 21, 2017 at 11:30 AM, Oleksandr Zaytsev <olk.zaytsev@gmail.com> wrote:
I started by implementing some simple threshold neurons. The current goal is a multilayer perceptron (similar to the one in scikit-learn), and maybe other kinds of networks, such as self-organizing maps or radial basis networks.
I could try to implement a deep learning algorithm, but the big issue with them is time complexity. Probably, it would require the use of GPU, or some advanced "tricks", so I should start with something smaller.
Also, I want to try different kinds of design approaches, including those that are not based on highly optimized vector algebra (I know that it might not be the best idea, but I want try it and see what happens). For example, a network, where each neuron is an object (normally the whole network is represented as a collection of weight matrices). It might turn out to be very slow, but more object-friendly. For now it's just an idea, but to try something like that I would need a small network with 1-100 neurons.
Yours sincerely, Oleksandr
Excellent Guillermo! I also wanted to play with the Mnist dataset. I will try your code Alexandre -- _,.;:~^~:;._,.;:~^~:;._,.;:~^~:;._,.;:~^~:;._,.;: Alexandre Bergel http://www.bergel.eu ^~:;._,.;:~^~:;._,.;:~^~:;._,.;:~^~:;._,.;:~^~:;.
On Mar 21, 2017, at 8:24 AM, Guillermo Polito <guillermopolito@gmail.com> wrote:
Hi Oleksandr,
I'm working half-time on a team doing simulations of spiking neural networks (on scala). Since the topic is "new" to me, I started following a MOOC on traditional machine learning and putting some of my code in here:
https://github.com/guillep/neural-experiments <https://github.com/guillep/neural-experiments>
Also, I wanted to experiment on hand-written characters with the Mnist dataset (yann.lecun.com/exdb/mnist/ <http://yann.lecun.com/exdb/mnist/>) so I wrote a reader for the IDX format to load the dataset.
https://github.com/guillep/idx-reader <https://github.com/guillep/idx-reader>
If you want I can take a look and we can discuss further :)
Guille
On Tue, Mar 21, 2017 at 11:30 AM, Oleksandr Zaytsev <olk.zaytsev@gmail.com <mailto:olk.zaytsev@gmail.com>> wrote: I started by implementing some simple threshold neurons. The current goal is a multilayer perceptron (similar to the one in scikit-learn), and maybe other kinds of networks, such as self-organizing maps or radial basis networks.
I could try to implement a deep learning algorithm, but the big issue with them is time complexity. Probably, it would require the use of GPU, or some advanced "tricks", so I should start with something smaller.
Also, I want to try different kinds of design approaches, including those that are not based on highly optimized vector algebra (I know that it might not be the best idea, but I want try it and see what happens). For example, a network, where each neuron is an object (normally the whole network is represented as a collection of weight matrices). It might turn out to be very slow, but more object-friendly. For now it's just an idea, but to try something like that I would need a small network with 1-100 neurons.
Yours sincerely, Oleksandr
Having a neuron as an object is exactly what I have in my implementation. Sounds exciting! Share your code when ready! Eager to try it! Alexandre -- _,.;:~^~:;._,.;:~^~:;._,.;:~^~:;._,.;:~^~:;._,.;: Alexandre Bergel http://www.bergel.eu ^~:;._,.;:~^~:;._,.;:~^~:;._,.;:~^~:;._,.;:~^~:;.
On Mar 21, 2017, at 7:30 AM, Oleksandr Zaytsev <olk.zaytsev@gmail.com> wrote:
I started by implementing some simple threshold neurons. The current goal is a multilayer perceptron (similar to the one in scikit-learn), and maybe other kinds of networks, such as self-organizing maps or radial basis networks.
I could try to implement a deep learning algorithm, but the big issue with them is time complexity. Probably, it would require the use of GPU, or some advanced "tricks", so I should start with something smaller.
Also, I want to try different kinds of design approaches, including those that are not based on highly optimized vector algebra (I know that it might not be the best idea, but I want try it and see what happens). For example, a network, where each neuron is an object (normally the whole network is represented as a collection of weight matrices). It might turn out to be very slow, but more object-friendly. For now it's just an idea, but to try something like that I would need a small network with 1-100 neurons.
Yours sincerely, Oleksandr
participants (3)
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Alexandre Bergel -
Guillermo Polito -
Oleksandr Zaytsev