mohamedndiaye

3i026-Intelligence-artificielle-et-data-science

aact

Improving Public Access to Aggregate Content of ClinicalTrials.gov

AACT-Sample-Graphs

Example graphs of interventional trials in ClinicalTrials.gov 2008-2017

agents

TF-Agents is a library for Reinforcement Learning in TensorFlow

Alink

Alink is the Machine Learning algorithm platform based on Flink, developed by the PAI team of Alibaba computing platform.

analyse-R

Introduction à l'analyse d'enquêtes avec R et RStudio

AnIntroductionToDeepLearning

Code and accompanying data used in my book "An Introduction to Deep Learning"

api-sdk-python

Particeep API Client for Python

APITaxi

Le.Taxi: France Taxis Exchange Point

AutoTL

Automatic transfer learning for short text mining

mohamedndiaye/Alink

Alink is the Machine Learning algorithm platform based on Flink, developed by the PAI team of Alibaba computing platform.

mohamedndiaye/microservices

Example of Microservices written using Flask.

mohamedndiaye/mlcourse.ai

Open Machine Learning Course

mohamedndiaye/nameko-examples

Nameko microservices example

mohamedndiaye/py-faster-rcnn

Faster R-CNN (Python implementation) -- see https://github.com/ShaoqingRen/faster_rcnn for the official MATLAB version

mohamedndiaye/spring-and-hibernate-for-beginners

Source code for the course: Spring and Hibernate for Beginners

mohamedndiaye/azure-quickstart-templates

Azure Quickstart Templates

mohamedndiaye/Countries

Free legally receivable IPTV channels as .m3u for Kodi. :-)

mohamedndiaye/CS_Mobility_Service

Mobility simulations for CityScope as a web service

mohamedndiaye/data-science-complete-tutorial

For extensive instructor led learning

mohamedndiaye/django-grpc

Easy gRPC service based on Django application

mohamedndiaye/Federated-Learning-for-Human-Mobility-Models

Thanks to the proliferation of smart devices, such as smartphones and wearables, which are equipped with computation, communication and sensing capabilities, a plethora of new location-based services and applications are available for the users at any time and everywhere. Understanding human mobility has gain importance to offer better services able to provide valuable products to the user whenever it's required. The ability to predict when and where individuals will go next allows enabling smart recommendation systems or a better organization of resources such as public transport vehicles or taxis. Network providers can predict future activities of individuals and groups to optimize network handovers, while transport systems can provide more vehicles or lines where required, reducing waiting time and discomfort to their clients. The representation of the movements of individuals or groups of mobile entities are called human mobility models. Such models replicate real human mobility characteristics, enabling to simulate movements of different individuals and infer their future whereabouts. The development of these models requires to collect in a centralized location, as a server, the information related to the users' locations. Such data represents sensitive information, and the collection of those threatens the privacy of the users involved. The recent introduction of federated learning, a privacy-preserving approach to build machine and deep learning models, represents a promising technique to solve the privacy issue. Federated learning allows mobile devices to contribute with their private data to the model creation without sharing them with a centralized server. In this thesis, we investigate the application of the federated learning paradigm to the field of human mobility modelling. Using three different mobility datasets, we first designed and developed a robust human mobility model by investigating different classes of neural networks and the influence of demographic data over models' performance. Second, we applied federated learning to create a human mobility model based on deep learning which does not require the collection of users' mobility traces, achieving promising results on two different datasets. Users' data remains so distributed over the big number of devices which have generated them, while the model is shared and trained among the server and the devices. Furthermore, the developed federated model has been the subject of different analyses including: the effects of sparse availability of the clients; The communication costs required by federated settings; The application of transfer-learning techniques and model refinement through federated learning and, lastly, the influence of differential privacy on the model’s prediction performance, also called utility

mohamedndiaye/fullstack-angular-and-springboot

mohamedndiaye/grpc

The C based gRPC (C++, Python, Ruby, Objective-C, PHP, C#)

mohamedndiaye/iptv-channels

Collection of 6000+ free IPTV channels from all over the world

mohamedndiaye/JavaWebApplicationStepByStep

JSP Servlets Tutorial For Beginners - in 25 Steps

mohamedndiaye/json-server

Get a full fake REST API with zero coding in less than 30 seconds (seriously)

mohamedndiaye/maas-components

Mobility-as-a-service mobile SDKs & UI components

mohamedndiaye/maas-schemas

Mobility as a Service API - data model, tests, and validation

mohamedndiaye/microservices-demo

Sample cloud-native application with 10 microservices showcasing Kubernetes, Istio, gRPC and OpenCensus.

mohamedndiaye/Mobility-trend-reports---Apple

The CSV file and charts on this site show a relative volume of directions requests per country/region, sub-region or city compared to a baseline volume on January 13th, 2020. We define our day as midnight-to-midnight, Pacific time. Cities are defined as the greater metropolitan area and their geographic boundaries remain constant across the data set. In many countries/regions, sub-regions, and cities, relative volume has increased since January 13th, consistent with normal, seasonal usage of Apple Maps. Day of week effects are important to normalize as you use this data. Data that is sent from users’ devices to the Maps service is associated with random, rotating identifiers so Apple doesn’t have a profile of individual movements and searches. Apple Maps has no demographic information about our users, so we can’t make any statements about the representativeness of usage against the overall population.

mohamedndiaye/purerpc

Asynchronous pure Python gRPC client and server implementation supporting asyncio, uvloop, curio and trio

mohamedndiaye/python-betterproto

Clean, modern, Python 3.7+ code generator & library for Protobuf 3 and async gRPC

mohamedndiaye/Python-for-Beginners

Here you can find all the main Python files written throughout my free YouTube tutorial series Python for Beginners!

mohamedndiaye/spleeter

Deezer source separation library including pretrained models.

mohamedndiaye/TabularEditor

This is the code repository and issue tracker for Tabular Editor 2.X (free, open-source version). This repository is being maintained by Daniel Otykier.

mohamedndiaye/The-Java-Workshop

A New, Interactive Approach to Learning Java

mohamedndiaye/TOMP-API

Transport Operator to Mobility-as-a-Service Provider-API development for Mobility as a Service

mohamedndiaye/tutorial-1

Tutorials on machine learning, artificial intelligence, data science with math explanation and reusable code (in python and R)

mohamedndiaye/Uber-Supply-Demand-Gap

Uber Technologies, Inc. (Uber) is an American mobility as a service provider. EDA and data visualisation is used to systematically study the gap between demand and supply of Uber cabs running between the city and the airport and address the problem Uber is facing - driver cancellation and non-availability of cars leading to loss of potential revenue.