Why money can’t buy you happiness.

It can’t. It's simple to understand. But it can buy you pleasure, though. But that’s also limited. After buying the best of the things, you would still become unhappy after some time when the dopamine goes down. Also, an observation can be made that you can’t achieve the newer levels of happiness highs if you never hit the newer lows of sadness.

So, materialistic happiness (which all can be bought) is limited. Therefore, to lead a happy life what one can aim for is a never-ending mission like ending poverty or hunger or ignorance. Such problems are so big that…


This is the writeup for the Deep RL project based on the NVidia open source project “jetson-reinforcement” developed by Dustin Franklin. The goal of the project is to create a DQN agent and define reward functions to teach a robotic arm to carry out two primary objectives:

  1. Have any part of the robot arm touch the object of interest, with at least a 90% accuracy.
  2. Have only the gripper base of the robot arm touch the object, with at least an 80% accuracy.

Hyperparameters tuning was the most challenging and time taking part of the project.

Reward Functions

The design of the…


Image source: https://www.magic-love-spells.com/old-witchcraft-secrets/

This article is based on the research paper named A Few Useful Things to Know about Machine Learning by Pedro Domingos.

Machine learning as a discipline allows its practitioner to solve problems just by generalizing from provided example solutions. The biggest advantage of this approach is that this prevents the need for explicitly writing rules of the solution.

When we don’t write a write the rules for the solution, we, by definition, can’t introduce bugs in that solution.

Problem

Needless to say, this sorcery comes with its own heap of shortcomings. Without the possession of the darker “folk secrets”, the ML projects end up in these scenarios:

  1. Taking way more time than estimated.
  2. Giving sub-optimal results

Let’s start revealing some of…


The mains steps in this perception Pipeline

RGBD camera feed from ASUS Xtion

1. Pipeline for filtering and RANSAC plane fitting implemented.

Statistical Outlier Filter


In this project, a network will be trained for classifying real-world objects into predefined classes. In addition to training a network on the supplied dataset, a different network will be chosen and trained using self-acquired data. The quality and quantity of data acquired will be discussed.

The network would be able to successfully classify the objects in the self-collected data into 4 classes with reasonable accuracy.

Introduction

The classification of objects in the 2D image had been tried to be solved by a lot of classical methods but deep learning was able to become the new State of the Art technology…


This is the time to think about how I input to the computer for getting work done by it. The Jeff Atwood blog on keyboard made me think about it.

I wanted to know which mouse could I buy to increase my productivity. There were some very great looking (and ergonomic) options like the Logitech MX Master, MX Anywhere, unconventional Anker Vertical Mouse. They all looked like noble choices(with noble prices).

As always, I went to reddit to find better reviews about these. I found what mouse do the seasoned programmers like to use for their everyday use. Answers ranged…


So, I was stuck in tuning the hyperparameters for a DQN agent when a problem struck me! I needed graphs to reliably compare as well as view the whole performance of the agent in all the episodes. gnuplots was the way forward. Lightweight, simple and fast.

gnuplots

The syntax is as follows:

plot 'plotRewards.dat' using 1:2 with lines
pause 0.5
reread
set autoscale

The plotRewards.datis the file storing the data that was written it the relevent program in the format of every datax y on a new line.

The pauselets you specify the break in seconds (Self-explanatory).

Coming to reread is…


Q. Why use this algorithm?
A. It is useful for *feature detection* and *feature description*.

Q. What are features? Why should I care about them?
A. Features are points of interest about the object that can be tracked. If we detect the correct features and track them, we can achieve a number of goals: Object detection: Using a training image, the good features of the object can be stored. When viewing the test image, we can look for the same features. …

Shivangg Tripathi

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