francismontalbo Goto Github PK

followers: 157.0 following: 6.0 repos: 56.0 gists: 0.0

Name: Francis Jesmar Montalbo

Type: User

Bio: Doctor in Information Technology class of 2021. Deep Learning; Machine Learning; Computer Vision; Data Science

Twitter: francismontalbo

Location: Philippines

Blog: https://francismontalbo.github.io

Hi there 👋

I'am Dr. Francis Jesmar P. Montalbo (FJP Montalbo), a researcher, programmer, and a data science enthusiast working in the field of Artifical Intelligence 🧠, specifically Deep Learning 🤖.

Here is my Google Scholar: Google Scholar Profile 🚀

PLEASE USE DARK MODE! # ⚠️

🔭 I’m currently working on research projects about Deep Learning and Computer Vision.
🌱 I’m currently learning Knowledge Distillation, Generative Adversarial Networks (GANs), Attention and Transformer networks for vision.
👯 I’m looking to collaborate on a research involving my research interest.
🤔 I’m looking for help in learning what I need to learn.
💬 Ask me about ideas!
📫 How to reach me: [email protected] or [email protected]
😄 Pronouns: I Me You
⚡ Fun fact: I love playing games and fitness.

Francis Jesmar Montalbo's Projects

attention-is-all-you-need-paper

Implementation of Vaswani, Ashish, et al. "Attention is all you need." Advances in neural information processing systems. 2017.

best_ai_papers_2022

A curated list of the latest breakthroughs in AI (in 2022) by release date with a clear video explanation, link to a more in-depth article, and code.

capslayer

CapsLayer: An advanced library for capsule theory

cascaded-fcn

Source code for the MICCAI 2016 Paper "Automatic Liver and Lesion Segmentation in CT Using Cascaded Fully Convolutional NeuralNetworks and 3D Conditional Random Fields"

covid19_diagnosis_and_analysis_of_truncated_dcnns

deep-neural-ensembles-toward-malaria-parasite-detection-in-thin-blood-smear-images

This study evaluates the performance of custom and pretrained CNNs and construct an optimal model ensemble toward the challenge of classifying parasitized and normal cells in thin blood smear images. The results obtained are encouraging and superior to the state-of-the-art.