Soyabul Islam Lincoln's repositories
awesome-notebooks
+100 awesome Jupyter Notebooks templates, organized by tools, published by the Naas community, to kickstart your data projects in minutes. 😎
Deep-Learning-Based-Radio-Signal-Classification
Final Project for AI Wireless
EmotionsInTheWild-CNN-Benchmarks
Emotion (Context + Facial) recognition in the wild using ConvNets (EfficientNet, ResNet, ResNext)
geospatial-data-catalogs
A list of open geospatial datasets available on AWS, Earth Engine, Planetary Computer, NASA CMR, and STAC Index
5G-NR-data-generato
The source code of the paper "5G MIMO-CSI: a data generator configuring to 5G NR channel standard and its application" is provided in the warehouse, and the data generator can be downloaded for free by researchers
annotated_deep_learning_paper_implementations
🧑🏫 59 Implementations/tutorials of deep learning papers with side-by-side notes 📝; including transformers (original, xl, switch, feedback, vit, ...), optimizers (adam, adabelief, ...), gans(cyclegan, stylegan2, ...), 🎮 reinforcement learning (ppo, dqn), capsnet, distillation, ... 🧠
Applied-Deep-Learning
Applied Deep Learning Course
COSCO
[TPDS'21] COSCO: Container Orchestration using Co-Simulation and Gradient Based Optimization for Fog Computing Environments
Deep-Reinforcement-Learning-Hands-On
Hands-on Deep Reinforcement Learning, published by Packt
detoxify
Trained models & code to predict toxic comments on all 3 Jigsaw Toxic Comment Challenges. Built using ⚡ Pytorch Lightning and 🤗 Transformers. For access to our API, please email us at contact@unitary.ai.
EEG_real_time_seizure_detection
Real-Time Seizure Detection using EEG: A Comprehensive Comparison of Recent Approaches under a Realistic Setting (CHIL 2022)
gradsflow
An open-source AutoML Library in PyTorch
heartrate_analysis_python
Python Heart Rate Analysis Package, for both PPG and ECG signals
imgaug
Image augmentation for machine learning experiments.
ivy
The Unified Machine Learning Framework
MoDANet
In the paper, a multi-task deep convolutional neural network, namely MoDANet, is proposed to perform modulation classification and DOA estimation simultaneously. In particular, the network architecture is designed with multiple residual modules, which tackle the vanishing gradient problem. The multi-task learning (MTL) efficiency of MoDANet was evaluated with different variants of Y-shaped connection and fine-tuning some hyper-parameters of the deep network. As a result, MoDANet with one shared residual module using more filters, larger filter size, and longer signal length can achieve better performance of modulation classification and DOA estimation, but those might result in higher computational complexity. Therefore, choosing these parameters to attain a good trade-off between accuracy and computational cost is important, especially for resource-constrained devices. The network is investigated with two typical propagation channel models, including Pedestrian A and Vehicular A, to show the affect of those channels on the efficiency of the network. Remarkably, our work is the first DL-based MTL model to handle two unrelated tasks of modulation classification and DOA estimation. Please cite the papar as:
SA-UNet
The open source code of SA-UNet: Spatial Attention U-Net for Retinal Vessel Segmentation.
start-machine-learning
A complete guide to start and improve in machine learning (ML), artificial intelligence (AI) in 2022 without ANY background in the field and stay up-to-date with the latest news and state-of-the-art techniques!
vision
Datasets, Transforms and Models specific to Computer Vision
yolov5
YOLOv5 🚀 in PyTorch > ONNX > CoreML > TFLite