awesome-AI-for-time-series-papers
qingsongedu
1,636estrellas
A professional list of Papers, Tutorials, and Surveys on AI for Time Series in top AI conferences and journals.
Aprendizaje automático · GitHub
Explora herramientas para entrenar, evaluar y ejecutar modelos, y compara el interés de la comunidad.
Proyectos útiles de GitHub
4,851 proyectos
time-series-transformers-review
qingsongedu
3,026estrellas
A professionally curated list of awesome resources (paper, code, data, etc.) on transformers in time series.
SoTA-Point-Cloud
QingyongHu
1,631estrellas
🔥[IEEE TPAMI 2020] Deep Learning for 3D Point Clouds: A Survey
Entity
qqlu
1,055estrellas
EntitySeg Toolbox: Towards Open-World and High-Quality Image Segmentation
awesome-civil-engineering
QuantumNovice
118estrellas
Awesome civil engineering software and programming reosurces
qxresearch-event-1
qxresearch
3,038estrellas
Python hands on tutorial with 50+ Python Application (10 lines of code) By @xiaowuc2
point2mesh
ranahanocka
1,250estrellas
Reconstruct Watertight Meshes from Point Clouds [SIGGRAPH 2020]
tinyforge
ranausmanai
71estrellas
A tiny model that teaches itself to code better. On your laptop. No cloud. No teacher model. No human feedback.
ai-art-generator
rbbrdckybk
621estrellas
For automating the creation of large batches of AI-generated artwork locally.
colorization
richzhang
3,474estrellas
Colorización automática mediante redes neuronales profundas. «Colorful Image Colorization». En ECCV, 2016.
deep-learning-wizard
ritchieng
881estrellas
Open source guides/codes for mastering deep learning to deploying deep learning in production in PyTorch, Python, Apptainer, and more.
Vectra-Core
ronitgupta138
65estrellas
⚡ In-Memory Vector Search Engine & HNSW ANN Graph in Java 21 Loom. Sub-millisecond KNN retrieval, 8-bit scalar quantization (SQ8), 16,000+ QPS.
MixTeX-Latex-OCR
RQLuo
1,636estrellas
MixTeX multimodal LaTeX, ZhEn, and, Table OCR. It performs efficient CPU-based inference in a local offline on Windows.
- LaTeX Formula Recognition: Accurately recognizes complex LaTeX mathematical formulas, ensuring mathematical expression accuracy.
- Table Recognition: Efficiently processes and recognizes various tables, generating corresponding LaTeX table code.
R-sharp
rsharp-lang
36estrellas
R# language is a kind of CRAN GNU R liked vectorized language implements on .NET environment for the bioinformatics data analysis
ruv-FANN
ruvnet
393estrellas
A blazing-fast, memory-safe neural network library for Rust that brings the power of FANN to the modern world.
- Default features - Enable model I/O, serialization, compression, logging, and parallel-related utilities. CPU inference does not require a GPU.
- io - Enables file formats and training-data I/O. Keep bounds on bytes, decompressed data, and declared model dimensions when loading external data.
58estrellas
On-device subject cutouts, image labels, and OCR for React Native. Apple Vision (iOS) · Google ML Kit (Android). Built with Nitro Modules.
- removeBackground Subject cutout — trim, export PNG/JPEG, optional mask classifyImage Ranked labels — offline on both platforms readText OCR — Latin / CJK / Devanagari on Android; Vision on iOS 18+ analyzeImage All three from a single decode Cutout and OCR results live on the native side until you export them. Call dispose() when done.
- supportsBackgroundRemoval - Cutouts available
MrD-Deep-Trainer
Sandeep9026
56estrellas
MrD Deeplearning Trainer Tool 2026: Open Source AI Model Training Suite for Developers
dire-jax
sashakolpakov
29estrellas
DImensionality REduction in JAX
ANEForge
sbryngelson
72estrellas
Python runtime for the Apple Neural Engine
sequence-labeling-BiLSTM-CRF
scofield7419
855estrellas
The BiLSTM-CRF model implementation in Tensorflow, for sequence labeling tasks.
Brain-Tumor-Segmentation
sdsubhajitdas
297estrellas
Brain Tumor Segmentation done using U-Net Architecture.
VehicleOrientationDataset
sekilab
91estrellas
The vehicle orientation dataset is a large-scale dataset containing more than one million annotations for vehicle detection with simultaneous orientation classification using a standard object detection network.
217estrellas
Data science for beginners involves learning to extract insights from data using statistics, programming (Python/R), and visualization. Key steps include data collection, cleaning, analysis, modeling, and communicating findings. Beginners should start with Python, basic math (linear algebra/calculus), and build projects to create a portfolio.
Machine-Learning-Roadmap
shanmukh05
534estrellas
A roadmap for getting started with Machine Learning