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Mohd Fitri Alif Bin Mohd Kasai's Projects

invasive_species_monitoring icon invasive_species_monitoring

Implemented convolutional neural network GoogLeNet with Inception modules and Ensemble of Inception V3, Xception, ResNet50 using transfer learning to detect invasive hydrangea in Brazilian national forest images dataset from Kaggle. Programmed in Keras using TensorFlow backend.

inventory icon inventory

Machine learning from grocery store reciepts

inyourface icon inyourface

Data, Code and Outputs for our manuscript "In your face: The biased judgement of fear-anger expressions in violent offenders."

ipbemonets icon ipbemonets

IPB EmoNets: a Deep Learning Approach for Emotion Recognition and Analysis

ironcar icon ironcar

🏎️ Mini self-driving car for {curious, passionnate} people.

is7033 icon is7033

PhD Seminar Course on advanced Artificial Intelligence algorithms by Dr. Paul Rad @ UT San Antonio.

isic-archive icon isic-archive

International Skin Imaging Collaboration: Melanoma Project

jaffe-feature icon jaffe-feature

Feature vectors extracted from JAFFE, using a OpenCV based library 'stasm'. The JAFFE database is not included due to permission issues.

jaffe-tensorflow icon jaffe-tensorflow

Crude CNN using tensorflow for facial emotion recognition on the JAFFE dataset

jeffheaton-book-code icon jeffheaton-book-code

Source code from my older (pre Artificial Intelligence for Humans books) books. I am no longer updating these older editions.

jetbot icon jetbot

An educational AI robot based on NVIDIA Jetson Nano.

jetracer icon jetracer

An autonomous AI racecar using NVIDIA Jetson Nano

jetson-car icon jetson-car

Autonomous Racing Car using NVIDIA Jetson TX2 using end-to-end training approach

jetson-inference icon jetson-inference

Guide to deploying deep-learning inference networks and deep vision primitives with TensorRT and NVIDIA Jetson.

jupyter icon jupyter

Jupyter metapackage for installation, docs and chat

kaggle-seeclickfix-ensemble icon kaggle-seeclickfix-ensemble

Prize winning solution to the SeeClickFix contest hosted on Kaggle, developed by teammates Bryan Gregory and Miroslaw Horbal. The purpose of the contest was to train a model (as scored by RMSLE) using supervised learning that will accurately predict the views, votes, and comments that an issue posted to the www.seeclickfix.com website will receive. My teammate and I used this ensemble code base to combine our top ranked individual models to create a prize-winning solution, defeating >500 other teams and winning a prize of $1,000.

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