Judithcodes

JUDITH NJOKU's repositories

Intelligent-Video-Analytics-with-NVIDIA-Jetson-and-Microsoft-Azure

A repository demonstrating an end-to-end architecture for Intelligent Video Analytics using NVIDIA hardware with Microsoft Azure

Language:TypeScriptMIT1 10

Radio-Modulation-Recognition-Networks

Radio modulation recognition with CNN and CLDNN networks

Language:Jupyter NotebookMIT1 10

RF_modulation_classification

A project on RF modulation classification using different neural architectures and RF signal representations.

Language:Jupyter Notebook1 10

Text-Emotion-Recognition

A repository for recognizing emotion in tweets

Language:Jupyter Notebook1 20

AUGMENTATION_GAN

GAN-based Synthetic Medical Image Augmentation for increased CNN Performance in COVID-19 Classification

Language:Jupyter NotebookMIT010

Combined-CNN-RNN-for-emotion-recognition

Predict valence and arousal from video data. Separate training of CNN and RNN. Feed RNN with simple feature vectors extracted from frames. Feature extraction is done by a simple CNN model.

MIT000

Complete-Python-3-Bootcamp

Course Files for Complete Python 3 Bootcamp Course on Udemy

Language:Jupyter Notebook010

Data

AI-based User Adaptive Interaction Laboratory

010

DL-based-Emotion-recognition-from-EEG

For more accurate data understanding I have applied single as well as multi-layer neural network for this task

Language:PythonMIT010

emotion-label-semantics

This repository provides the code and dataset for the work published in the paper - Modeling Label Semantics for Predicting Emotional Reactions

Language:Python010

Face_Recognition

<NOT ACTIVELY MAINTAINED>A light weight face recognition implementation using a pre-trained facenet model

Language:Python010

handson-ml3

A series of Jupyter notebooks that walk you through the fundamentals of Machine Learning and Deep Learning in Python using Scikit-Learn, Keras and TensorFlow 2.

Apache-2.0000

HSE_AffectiveComputing

Affective computing course, Higher School of Economics (HSE), lecturer Olga Perepelkina, 2020

Language:Jupyter Notebook010

Intelligent-model-of-Communication-system-simulation-based-on-auto-encoding-deep-learning-

Used a Generative Adversarial Network(GAN) to simulate a optical communication system; Applying auto-encoding deep-learning method to build an Intelligent model of Communication system in order to improve its information rate and symbol coding method; laying a good foundation for the development of intelligent fiber communication systems;

Language:Jupyter Notebook010