Making Sense of Big Data

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Figure 1: Removal of the CMS beam pipe (Image by Maximilien Brice/Julien Ordan/CERN — Source: CERN )

The detection of muons is an important task in the CMS experiment at CERN (European Organization for Nuclear Research). Muons are usually expected to be produced by many physical experiments studied in depth at the CMS physics program at CERN. For example, one way to study the famous Higgs Boson particle is through a decay channel where the Higgs Boson decays into four muons.

Since muons can penetrate several meters of iron with no significant energy loss, they are hardly stopped by any layer of the CMS system. Therefore, the muon detectors are the outermost ones, far from the interaction point. The current muon transverse momentum detector, the Barrel Muon Track Finder, uses Lookup Tables to estimate the transverse momentum of the muons. The latter is measured using the pseudo-rapidity eta, a spatial quantity related to the angle at which the charged particles emerge from these collisions, and the azimuth angle phi, the angle that the collision paths make with the z-axis. …

How we tackled plant images segmentation problem with Deep Learning.

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Photo by Josefin on Unsplash

Identifying weed plants in cultivated field is a time-intensive task. Automating such a task can drastically reduce the time needed to identify and remove weeds and thus increase the yield and the productivity of the workers. In our latest project, my colleague Rawane Madi and I worked on a plant segmentation task that uses Deep Learning to identify weeds and their stems from crops. …

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Figure 1: Photo by Ricardo Cruz on Unsplash

Hello, Hello! Welcome back to the second part of my series on How to structure RL projects !

  1. Start the Journey: Frame your Problem as an RL Problem
  2. Choose your Weapons: All the Tools You Need to Build a Working RL Environment (We are Here!)
  3. Face the Beast: Pick your RL (or Deep RL) Algorithm
  4. Tame the Beast: Test the Performance of the Algorithm
  5. Set it Free: Prepare your Project for Deployment/Publishing

In this post, we discuss the second part of this series:

Choose your Weapons: All the Tools You Need to Build a Working RL Environment

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Figure 1: Photo by Nik Shuliahin on Unsplash

Ten months ago, I started my work as an undergraduate researcher. What I can clearly say is that it is true that working on a research project is hard, but working on an Reinforcement Learning (RL) research project is even harder!

What made it challenging to work on such a project was the lack of proper online resources for structuring such type of projects;

  • Structuring a Web Development project? Check!
  • Structuring a Mobile Development project? Check!
  • Structuring a Machine Learning project? Check!
  • Structuring a Reinforcement Learning project? Not really!

To better guide future novice researchers, beginner machine learning engineers, and amateur software developers to start their RL projects, I pulled up this non-comprehensive step-by-step guide for structuring an RL project which will be divided as…


Anis Ismail

Undergraduate Researcher | Machine Learning Engineer | Computer Engineering Student @ LAU |

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