Accel Brain Code: From Proof of Concept to Prototype.¶
The purpose of this repository is to make prototypes as case study in the context of proof of concept(PoC) that I have written in my website: Accel Brain (Japanese).
The basic theme in my website is multiple perspectives methodology, very abstract theory, extreme aesthetics, recognition of melancholic history, and generalized design philosophy of algorithms and architectures. All code was implemented for the purpose of proof-of-concept, demonstration experiment, or verification test.
pysummarization is Python3 library for the automatic summarization, document abstraction, and text filtering.
The function of this library is automatic summarization using a kind of natural language processing. This library enable you to create a summary with the major points of the original document or web-scraped text that filtered by text clustering.
Full documentation is available on https://code.accel-brain.com/Automatic-Summarization/ . This document contains information on functionally reusability, functional scalability and functional extensibility.
AccelBrainBeat is a Python library for creating the binaural beats or monaural beats. You can play these beats and generate wav files. The frequencys can be optionally selected.
This Python script enables you to handle your mind state by a kind of "Brain-Wave Controller" which is generally known as Biaural beat or Monauarl beats in a simplified method.
Full documentation is available on https://code.accel-brain.com/Binaural-Beat-and-Monaural-Beat-with-python/ . This document contains information on functionally reusability, functional scalability and functional extensibility.
This is the simple card box system that make you able to find and save your ideas.
pydbm is Python3 library for building restricted boltzmann machine, deep boltzmann machine, and multi-layer neural networks.
In relation to my Automatic Summarization Library, it is important for me that the models are functionally equivalent to stacked auto-encoder. The main function I observe is the same as dimensions reduction(or pre-training). But the functional reusability of the models can be not limited to this. These Python Scripts can be considered a kind of experiment result to verify effectiveness of object-oriented analysis, object-oriented design, and GoF's design pattern in designing and modeling neural network, deep learning, and reinforcement-Learning.
Full documentation is available on https://code.accel-brain.com/Deep-Learning-by-means-of-Design-Pattern/ . This document contains information on functionally reusability, functional scalability and functional extensibility.
These Python Scripts create a template method pattern for implementing a Q-learning.
Considering many variable parts and functional extensions in the Q-learning paradigm, I implemented these Python Scripts for demonstrations of commonality/variability analysis in order to design the models.
- GNU General Public License v2.0