lakshyapriyadarshime Goto Github PK

abstractions-discussions

abstractions-discussions

arxiv-sanity-preserver

Web interface for browsing, search and filtering recent arxiv submissions

blockchain-ruby

My exercise to translate Daniel van Flymen brilliant post "Learn Blockchain by Building One" (https://hackernoon.com/learn-blockchains-by-building-one-117428612f46) to Ruby

chaser-drone

Chaser Drone based on ROS, ROS WhyCon technology, simulated in Gazebo real-world physics simulator. WhyCon employs flood-filling image-processing algorithm to detect markers. Proportional–integral–derivative control algorithm is employed to control drone motion. All codes implemented in Python and integrated with ROS Core and ROS WhyCon .

dlwithtf

TensorFlow for Deep Learning Book

git-learn

gochain

A basic implementation of Blockchain

gravitational-waves-data-analysis

Data analysis of a compact binary system (black hole / neutron star) by modeling the linear combination of gravitational wave polarizations. The wave signal is computed as a real-time series and expressed as function of time-variant amplitude and phase. The analysis applies Fourier transformation and Gaussian normalization to wave signals, and provides data visualization for amplitude, phase, wave-signal and noise. All codes have been implemented in Python3.

iris-machine-learning

Statistical Analysis on Iris Dataset and performance evaluation of Machine Learning Algorithms based on precision and accuracy metric

lakshyapriyadarshime.github.io

Personal blog

phyllotaxis

Project: Phyllotaxis: Draw flowers using mathematics

sentiment-analysis-facebook

Sentiment Analysis

xor-neural-network

A multi-layered neural network that models the fundamental logic gate XOR by modeling given input and output parameters over a set of boolean values ( logic 0 and logic 1 ). The mathematical function sigmoid(x) is set as the activation function and randomly-initialized weight / bias matrix are used, which improve along with the process-cycle, by deploying a batch gradient descent with a learning rate of 0.1. The code for the neural network is implemented in Python3.