Burhan's repositories
ubd-herbarium-repository
This repo consist of computer vision and machine learning research projects for UBD-herbarium including the dataset used.
Semantic-Segmentation-Suite
Semantic Segmentation Suite in TensorFlow. Implement, train, and test new Semantic Segmentation models easily!
awesome-machine-learning-interpretability
A curated list of awesome machine learning interpretability resources.
Mask_RCNN
Mask R-CNN for object detection and instance segmentation on Keras and TensorFlow
artificio
Deep Learning Computer Vision Algorithms for Real-World Use
awesome-deep-vision
A curated list of deep learning resources for computer vision
awesome-machine-learning
A curated list of awesome Machine Learning frameworks, libraries and software.
awesome-production-machine-learning
A curated list of awesome open source libraries to deploy, monitor, version and scale your machine learning
Awesome-U-Net
Official repo for Medical Image Segmentation Review: The success of U-Net
cs-video-courses
List of Computer Science courses with video lectures.
cs231n.github.io
Public facing notes page
data_science_blogs
A repository to keep track of all the code that I end up writing for my blog posts.
deep-image-prior
Image restoration with neural networks but without learning.
DeepLearningImplementations
Implementation of recent Deep Learning papers
gretel-blueprints
Public blueprints for data use cases
industry-machine-learning
A curated list of applied machine learning and data science notebooks and libraries across different industries.
inpainting-gmcnn-keras
Keras implementation of "Image Inpainting via Generative Multi-column Convolutional Neural Networks" paper published at NIPS 2018
KAIST_2019_Deep-Learning_HW3
A PyTorch Implementation of "Extracting and Composing Robust Features with Denoising Autoencoders"
longitmssegm
Repository for MS lesion segmentation using deep learning
Object-detection-with-deep-learning-and-sliding-window
Introduces an approach for object detection in an image with sliding window. The repository contains three files, make_data.py reads the image in gray scale and converts the image into a numpy array. The labels are also appended based on the file name. In this case, if the file name starts with "trn", then 1 is appended else 0. Finally, all the images and labels are saved into .npy file. The test-model-1.py file loads the images and converts the labels into two categories as we are doing binary classification of images. The model is built using keras with theano as backend. In this case, the best training accuracy was 80% since the data was just 500 images and the testing accuracy was 67%
primu_nnUNet
Primus version of nnUNet
smote_variants
A collection of 85 minority oversampling techniques (SMOTE) for imbalanced learning with multi-class oversampling and model selection features
stylegan
StyleGAN - Official TensorFlow Implementation
Time-Series-Analysis
code and data for the time series analysis vids on my YouTube channel
unet-3D
Keras implementation of a 2D/3D U-Net with Additive Attention, Inception, and Recurrence functions provided