There are many flavors of games in Game Theory which are interesting from Machine Learning perspectives, especially from multi-agent Reinforcement Learning applications. Here is the summary of multiple game types are if MinMax algorithm works and what type of strategy one needs to employ.
Category: Data Science
Entropy is the fundamental unit of information in Information Theory and is extensively useful in Machine Learning. Let us introduce the concepts: Entropy, Joint Entropy, Mutual Information.
This is the recommended template for a Project Proposal for a freelance Machine Learning project. This is my personal choice, and definitely depending on project requirements, you might need to explicitly modify change/modify/add to this list. One size does not always fit all for Machine Learning projects. But anyway this could be a good starting point.
Quick introduction to basic Reinforcement Learning algorithms including Bellman Equation, Policy Iteration, Value Iteration, and Q-Learning
Collection of useful tips when working with Big Data tools including Hadoop, Hive, Spark
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Big Data Handy References
Integrating Apache Hive with Kafka, Spark, and BI: https://docs.cloudera.com/HDPDocuments/HDP3/HDP-3.1.5/integrating-hive/content/hive_hivewarehousesession_api_operations.html
An important Machine Learning concept is Reinforcement Learning which is different from the more common Supervised or Unsupervised Learning models. In Supervised learning, you have the labels for training, in Unsupervised learning, there is no labeled data. Reinforcement Learning falls in between the two because it does not have a label but it learns from […]
Work-in-progress. I will write more about each approach later in details. Just summarizing the tools for connecting to Hadoop and running geospatial processing on a large dataset. I am working on a ~100 GB Hive Table which is just a small subset of the original dataset http://geospark.datasyslab.org/ https://pypi.org/project/geopyspark/ https://github.com/Esri/gis-tools-for-hadoop/wiki Kinetica GPU Database – Graph solver […]
Objective Let us compare the memory consumption of Python PANDAS library. Methodology Small script called memtest.py: @profile def a(): import pandas if __name__==”__main__”: a() Test it with $python -m memory_profiler memtest.py Results: Output: The increment of memory usage in line#4 shows, it took 36.527MB to import pandas on my machine. What does your benchmarking result […]