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  • Carleton University
  • Ottawa, Canada

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sachag678/README.md

Hello there.

I enjoy working on understanding the fundamentals of AI:

  • Reinforcement learning
  • Bayesian reasoning and statistics

Some recent projects are:

  • Implementing Gaussian process based hyper parameter optimization using expected improvement as the aquisition function. Link
  • Implementing the standard line search and trust region search methods for optimization: Link

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  1. Java Learning from observation Framework using CBR and Bayesian Networks

    Java 4 4

  2. Contains baseline implementations of all RL algorithms using tabular and function approximations. Algorithms such as TD(0), MC, SARSA, Q-Learning and Policy Gradient methods.

    Jupyter Notebook 6 2

  3. A framework that focuses on using bayesian and Dynamic Bayesian Networks to perform Learning from observation on Discrete Domains

    Java 1

  4. This will contain all the code I write during the next 100 days

    Python

  5. Python Projects using sckit-learn, tensorflow, requests, unittesting, time series prediction, weather apps, multi threading

    Python 1

  6. Contains code for teaching an agent the rules of Rock Paper Scissors

    JavaScript 4 5

47 contributions in the last year

Dec Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Mon Wed Fri
Activity overview
Contributed to sachag678/sachag678, sachag678/sacha-mvn, sachag678/100DaysofCode and 5 other repositories

Contribution activity

December 2021

sachag678 has no activity yet for this period.
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