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Holistic Understanding

Holistic understanding demands that we give the machine everything we have. Filtering or cleaning input data can lead to confusion in real-life scenarios. To enable true understanding, we should avoid heavy-handed heuristic cleanup of the input data.

— 𝘔𝘰𝘯𝘪𝘤𝘢 𝘈𝘯𝘥𝘦𝘳𝘴𝘰𝘯

Read more: The Red Pill of Machine Learning

What I'm working on

Play Chrobatos — a Resistor Color Number Game

Learn Latin

𝘛𝘩𝘪𝘯𝘬𝘪𝘯𝘨 𝘪𝘯 𝘊𝘪𝘳𝘤𝘭𝘦𝘴

Cogito Ergo Sum's Projects

face_info icon face_info

face recognition, detection of facial attributes (age, gender, emotion and race) for python.

faceswap-gan icon faceswap-gan

A denoising autoencoder + adversarial losses and attention mechanisms for face swapping.

facial-age-detection icon facial-age-detection

Developed an Image-based Classifier model using CNN,Keras and OpenCV to predict the Age of a person from an input image

facial-recognition icon facial-recognition

Recognizing faces in an image and predicting age, gender and emotion using Tensorflow.

facsvatar-blender icon facsvatar-blender

Companion Blender add-on to the FACSvatar project. Provides an interface in Blender and non-blocking data transfer.

factory_bot icon factory_bot

A library for setting up Ruby objects as test data.

factotum icon factotum

A system to programmatically run data pipelines

failpoint icon failpoint

An implementation of failpoints for Golang.

fairmot icon fairmot

This project provides an official implementation of our recent work on real-time multi-object tracking in videos. The previous works conduct object detection and tracking with two separate models so they are very slow. In contrast, we propose a one-stage solution which does detection and tracking with a single network by elegantly solving the alignment problem. The resulting approach achieves groundbreaking results in terms of both accuracy and speed: (1) it ranks first among all the trackers on the MOT challenges; (2) it is significantly faster than the previous state-of-the-arts. In addition, it scales gracefully to handle a large number of objects.

fairness.jl icon fairness.jl

Julia Toolkit with fairness metrics and bias mitigation algorithms

faiss icon faiss

A library for efficient similarity search and clustering of dense vectors.

fake-rs icon fake-rs

A library for generating fake data in Rust.

faker icon faker

Faker is a Python package that generates fake data for you.

faker-1 icon faker-1

A Python library for generating fake user data.

faker-2 icon faker-2

Python library to generate fake data

faker-3 icon faker-3

Random fake data and struct generator for Go.

falcon icon falcon

A Simple Yet Highly Powerful Package For Error Handling

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