ltoscano Goto Github PK

followers: 4.0 following: 2.0 repos: 500.0 gists: 7.0

Name: Lorenzo

Type: User

Bio: Data Scientist

Location: Milan

Blog: https://ltoscano.github.io

Lorenzo's Projects

machine-learning-for-trading

Udacity course on manipulating financial data in Python, computational investing, and algorithms for trading

machine-learning-mindmap

A mindmap summarising Machine Learning concepts, from Data Analysis to Deep Learning.

machine-learning-resources

This is a list of free resources to learn Machine Learning.

machine-learning-tutorials

machine learning and deep learning tutorials, articles and other resources

machine_learning_books

Machine_Learning Books downloaded from the internet.It covers concepts of machine learning from basic level to advanced level and doesn't mean to hurt anyone's copyright or authenticity

magpie

Deep neural network framework for multi-label text classification

mask_rcnn

Mask R-CNN for object detection and instance segmentation on Keras and TensorFlow

mastering-machine-learning-on-aws

Mastering Machine Learning on AWS, published by Packt

matchzoo

MatchZoo is a toolkit for text matching. It was developed to facilitate the designing, comparing, and sharing of deep text matching models.

maxmin-cnn

Implementation of Paper: "MaxMin Convolution Neural Networks for Image Classfication" in Keras.

metabase

The simplest, fastest way to get business intelligence and analytics to everyone in your company :yum:

metrics

Machine learning evaluation metrics, implemented in Python, R, Haskell, and MATLAB / Octave

minisom

:red_circle: MiniSom is a minimalistic implementation of the Self Organizing Maps

mir_robot

ROS support for the MiR Robots. This is a community project to use the MiR Robots with ROS. It is not affiliated with Mobile Industrial Robots.

ml-bootcamp

A series of tutorials complete with notebook to learn the basic principle of machine learning techniques

motion-planning-of-a-differential-drive-robot

The differential drive has an ESP32 board for wireless connectivity a Client-Server network is established between the server laptop and client ESP to transmit the coordinates to the robot. An overhead camera is used to visually survey the obstacle course and image processing is used to segment the obstacles and the robot from the captured images. Further, the obstacle course is used to make a "visibility graph" and finally "Dijkstra's shortest path algorithm" is used to search the shortest pah from the robot position to the goal position. Kinematic Equations of the differential drive are used to drive the robot on the path obtained. Finally, a pygame simulation of the robot movement is made to predict the behavior of the robot in real world and the robot is driven using this simulation.

ltoscano Goto Github PK

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