Artificial Intelligence / Machine Learning
@Expert Systems. Note: We
don't actually have Artificial Intelligence (yet). We have Machines that
Lyapunov functions. Because we always want the derivative of our cost function
to smoothly decend to a singal minimum.
"Bridging the Gaps Between Residual Learning,
Recurrent Neural Networks and Visual Cortex" This paper compares state of the art image processing ML and the human visual cortex, then uses the differences to better understand both and improve ML techniques.+
Recommended by Lloyd Moore of Seattle Robotics Society. He takes a very simplified approach, which doesn't require as much math for an intuitive understanding.+
https://youtu.be/v9M2Ho9I9Qo Excellent AI video. How to
learn without examples. +
Good series of articles.
Deep Learning 2017+
List of datasets. Sample data from many different areas to use in training
http://course.fast.ai/ Free Deep Learning course from
University of San Francisco+
https://github.com/libelo/py-ng-ml Repository of Python
versions of common Machine Learning methods from a popular online
Fully accesable research papers on AI.
http://spark.apache.org/ Apache Sparkâ¢
is a free, open source, fast and general engine for large-scale data processing.
Spark runs on Hadoop, Mesos, standalone, or in the cloud. It can access diverse
data sources including HDFS, Cassandra, HBase, and S3. Write applications
quickly in Java, Scala, Python, R. Run programs up to 100x faster than Hadoop
MapReduce in memory, or 10x faster on disk.+
The Apache Hadoop software library is a framework that allows for the distributed
processing of large data sets across clusters of computers using simple
programming models. It is designed to scale up from single servers to thousands
of machines, each offering local computation and storage. Rather than rely
on hardware to deliver high-availability, the library itself is designed
to detect and handle failures at the application layer, so delivering a
highly-available service on top of a cluster of computers, each of which
may be prone to failures.+
very high level overview of Machine Learning.
the XOR function makes fitting a line to the data impossible. Many times,
in the real world, conditions were extreems are bad, or good and the middle
condition is the opposite look like the XOR problem. Sometimes you can learn
the wrong answer faster than the right answer.
Why it's good to have extra layers in your learning system: Deep Learning.
History of AI.
https://www.youtube.com/watch?v=BCBZPtZCI7w Facial recognition
with Machine Learning.+
CMAC for Classification+
http://numenta.org/ NuPIC is an open source
project based on a theory of neocortex called Hierarchical Temporal Memory
Byte Magazine July 1979. "A Model of the Brain for Robot Control. Part 2"
By James Albus, inventor of the CMAC. Gives an overview of the idea and it's
Byte Magazine August 1979. "A Model of the Brain for Robot Control. Part
3" By James Albus, inventor of the CMAC. Tests the concept and expands on
Simple version of the CMAC C code. Easy to
http://www.ece.unh.edu/robots/cmac.htm C code for the CMAC
neural net alternative by James Albus.
simulating a nervous network on a processor A Self-Wiring Array of "Bicores"
for Robotic Control (cached
Simulation of Ants
A very good faker. Winner of the 1996 Turing test.
Overview of Machine learning with some technical details from SIGGRAPH 2020.
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