davidjegan

BakingPi.github.io

Baking world of Pi

Alexa-zappa-based-serverless-bot

Alexa-zappa-based-serverless-bot

amazon-sagemaker-examples

Example notebooks that show how to apply machine learning and deep learning in Amazon SageMaker

awesome-datascience

:memo: An awesome Data Science repository to learn and apply for real world problems.

AWS-EC2-Instance-Status-Automation

AWS-EC2-Instance-Status-Automation

CommonCrawlTutorial

A tutorial on how to use Common crawl for data extraction

davidjegan.github.io_archaic

Empirical Experiments

expose-cassandra-data

expose-cassandra-data

Game-play-using-Reinforced-Learning

This code addresses how we could teach an agent to navigate in a grid-world environment. In this modelled tom and jerry game, we apply reinforcement learning DQN (Deep Q-Network) to make the agent find the optimal shortest path from the goal(Jerry) to initial position(Tom) from its history of interaction with the environment. These two initial positions are deterministic.

serverless-slack-application

serverless-slack-application

davidjegan/CommonCrawlTutorial

A tutorial on how to use Common crawl for data extraction

davidjegan/Game-play-using-Reinforced-Learning

This code addresses how we could teach an agent to navigate in a grid-world environment. In this modelled tom and jerry game, we apply reinforcement learning DQN (Deep Q-Network) to make the agent find the optimal shortest path from the goal(Jerry) to initial position(Tom) from its history of interaction with the environment. These two initial positions are deterministic.

davidjegan/Alexa-zappa-based-serverless-bot

Alexa-zappa-based-serverless-bot

davidjegan/amazon-sagemaker-examples

Example notebooks that show how to apply machine learning and deep learning in Amazon SageMaker

davidjegan/awesome-datascience

:memo: An awesome Data Science repository to learn and apply for real world problems.

davidjegan/AWS-EC2-Instance-Status-Automation

AWS-EC2-Instance-Status-Automation

davidjegan/davidjegan.github.io_archaic

Empirical Experiments

davidjegan/expose-cassandra-data

expose-cassandra-data

davidjegan/serverless-slack-application

serverless-slack-application

davidjegan/AWS-EMR-Node-Calculator

AWS-EMR-Node-Calculator

davidjegan/BakingPi.github.io

Baking world of Pi

davidjegan/coding-interview-university

A complete computer science study plan to become a software engineer.

davidjegan/davidjegan.github.io

davidjegan/docker-cheat-sheet

Docker Cheat Sheet

davidjegan/docusaurus-tutorial

Docusaurus makes it easy to maintain Open Source documentation websites

davidjegan/easy-application

Over 400 software engineering companies that are easy to apply to

davidjegan/examples

Kubernetes application example tutorials

davidjegan/Fizzbuzz-using-Neural-Network

This code gives a contrast difference between the traditional programming methodology and Machine learning model. I have leveraged the Fizz-buzz problem to point out the intricacies and advantages ML (Software 2.0) proposes over conventional programming model (Software 1.0)

davidjegan/GoogleCloudArchitectProfessional

Resources to prepare for Google Certified Cloud Architect Professional Exam - 2017

davidjegan/Handwriting-Recognition-using-Linear-and-Logistic-Regression

This code addresses how we could identify the similarity between two handwritten samples using Linear regression, Logistic regression and Tensorflow methodologies. If the algorithm detects a match, then 1 is returned and similarily 0 is returned if there is dissimilarity between the two images. Though the results are scalar and discrete (0,1), we consider linear/logistic regression and tensorflow to obtain continuous value rather than a discrete on, to find the percent of match.

davidjegan/Handwritten-digit-detection-using-Ensemble-Learning

The code provides a detailed insight on how combination of Classifier algorithms can be used to recognize an handwritten digit image.

davidjegan/interview

Everything you need to prepare for your technical interview

davidjegan/LETOR-using-ML-Linear-Regression

The code provides a detailed insight on how Linear regression can be used to solve Learning to Rank problem. We leverage two methodologies., Closed form and Gradient Descent.

davidjegan/LoTR-Scapper

LoTR-Scapper built on Python

davidjegan/NLP-Playground

Snippets of NLP techniques I have played upon!

davidjegan/serverlessguru-david

A Repo consisting of various submissions for ServerlessGuru

davidjegan/spark

Apache Spark

davidjegan/Wegman-Customer-Router

Utilizing Wegman API to map its customers to the nearest Wegman Store