Name: KOUASSI Konan Jean-Claude
Type: User
Company: Ivorian Civil Service, Ministry of Environment
Bio: - Computer Scientist,
- Ph.D. student in AI at BIU-Madrid,
- Machine Learning Engineer,
- Networks Engineer,
- Passionate of Cognitive Computing Research
Location: Abidjan, Côte d'Ivoire
Blog: https://profiles.udacity.com/u/konanjeanclaudekouassi
KOUASSI Konan Jean-Claude's Projects
A Face-Recognition-In-Video project to apply face recognition insight video
The world's simplest facial recognition api for Python and the command line
A Lightened CNN for Deep Face Representation
An implementation of iPhone X's FaceID using face embeddings and siamese networks on RGBD images.
Tensorflow implementation of the FaceNet face recognizer
Visualizations for machine learning datasets
Facebook AI Research Sequence-to-Sequence Toolkit
A library for efficient similarity search and clustering of dense vectors.
Practical Deep Learning for Coders (fast.ai courses)
My implementations for the FAST AI courses
Fast R-CNN
Library for fast text representation and classification.
Tools for using a variational auto-encoder for latent image encoding and generation.
Feather: fast, interoperable binary data frame storage for Python, R, and more powered by Apache Arrow
Fuzzy Inference System Tool
A very simple framework for state-of-the-art Natural Language Processing (NLP)
Supporting code for my article on video streaming with Flask.
Flask app wrapper for convolutional neural network (CNN) model covered in lesson 2 of the fast.ai deep learning MOOC
FloydHub Documentation
Framework for building high-performance, high-fidelity iOS and Android apps.
A Toolkit for Modeling and Simulation of Resource Management Techniques in Internet of Things, Edge and Fog Computing Environments
:books: Freely available programming books
Vanilla GAN implemented on top of keras/tensorflow enabling rapid experimentation & research. Branches correspond to implementations of stable GAN variations (i.e. ACGan, InfoGAN) and other promising variations of GANs like conditional and Wasserstein.
Pytorch-based tools for visualizing and understanding the neurons of a GAN. https://gandissect.csail.mit.edu/
starter from "How to Train a GAN?" at NIPS2016
GAP is a gender-balanced dataset containing 8,908 coreference-labeled pairs of (ambiguous pronoun, antecedent name), sampled from Wikipedia for the evaluation of coreference resolution in practical applications.
Code for "Learning to Generate Reviews and Discovering Sentiment"
Collection of generative models, e.g. GAN, VAE in Pytorch and Tensorflow.
A Yeoman generator for your DoneJS application
A higher-level wrapper around the Github API. Intended for the browser.