Jehoiada Jackson's Projects
100 data puzzles for pandas, ranging from short and simple to super tricky (60% complete)
A curated list of awesome computer vision resources
Biorbd visualization toolkit
Build-related tools for TensorFlow
Bypass Paywalls web browser extension for Chrome and Firefox.
An image classification problem identifying the presence or absence of cactus in a 32 by 32 pixel image. The 17,500-image database was split 60% train, 20% validation, 20% test. Parallel processing was implemented for image processing and model building. Feature engineering included the creation edge features using the difference between adjacent pixels. Support vector machine, random forest, Xgboost, and convolutional neural network (CNN) models were fit. The CNN fit with TensorFlow and Keras outperformed all other models with 97.5% classification accuracy. By using a weighted average of the Xgboost, Random Forest, and CNN models a classification accuracy of 97.7% was achieved. This was a team project. I was responsible for the image processing, feature engineering, Random Forest, Xgboost, and weighted average models.
Deep Neural Network for Image Classification: Application
Code release for ConvNeXt model
Official implementation of CrossViT. https://arxiv.org/abs/2103.14899
Udacity Data Analysis Probability
Data Engineering Practice Problems
Free Data Engineering course!
Data modelling with Postgres
TFDS is a collection of datasets ready to use with TensorFlow, Jax, ...
Densely Connected Pyramid Dehazing Network (CVPR'2018)
an end to end dehaze tools
FAIR's research platform for object detection research, implementing popular algorithms like Mask R-CNN and RetinaNet.
Linux, Jenkins, AWS, SRE, Prometheus, Docker, Python, Ansible, Git, Kubernetes, Terraform, OpenStack, SQL, NoSQL, Azure, GCP, DNS, Elastic, Network, Virtualization. DevOps Interview Questions
Depthwise Over-parameterized Convolutional Layer
FFA-Net: Feature Fusion Attention Network for Single Image Dehazing
The open-source tool for building high-quality datasets and computer vision models
Official implementation of https://arxiv.org/abs/2105.08655 paper