Ecosyste.ms: Awesome

An open API service indexing awesome lists of open source software.

CNT-Guide

Carbon Nanotubes (CNTs) Guide
https://github.com/mikeroyal/CNT-Guide

  • MATLAB
  • MATLAB Documentation
  • Getting Started with MATLAB
  • MATLAB and Simulink Training from MATLAB Academy
  • MathWorks Certification Program
  • MATLAB Online Courses from Udemy
  • MATLAB Online Courses from Coursera
  • MATLAB Online Courses from edX
  • Building a MATLAB GUI
  • MATLAB Style Guidelines 2.0
  • Setting Up Git Source Control with MATLAB & Simulink
  • Pull, Push and Fetch Files with Git with MATLAB & Simulink
  • Create New Repository with MATLAB & Simulink
  • PRMLT
  • MATLAB and Simulink Services & Applications List
  • MATLAB in the Cloud - cloud) including [AWS](https://aws.amazon.com/) and [Azure](https://azure.microsoft.com/).
  • MATLAB Online™
  • Simulink - Based Design. It supports simulation, automatic code generation, and continuous testing of embedded systems.
  • Simulink Online™
  • MATLAB Drive™
  • MATLAB Parallel Server™
  • MATLAB Schemer
  • LRSLibrary - Rank and Sparse Tools for Background Modeling and Subtraction in Videos. The library was designed for moving object detection in videos, but it can be also used for other computer vision and machine learning problems.
  • Image Processing Toolbox™ - standard algorithms and workflow apps for image processing, analysis, visualization, and algorithm development. You can perform image segmentation, image enhancement, noise reduction, geometric transformations, image registration, and 3D image processing.
  • Computer Vision Toolbox™
  • Statistics and Machine Learning Toolbox™
  • Lidar Toolbox™ - camera cross calibration for workflows that combine computer vision and lidar processing.
  • Mapping Toolbox™
  • UAV Toolbox
  • Parallel Computing Toolbox™ - intensive problems using multicore processors, GPUs, and computer clusters. High-level constructs such as parallel for-loops, special array types, and parallelized numerical algorithms enable you to parallelize MATLAB® applications without CUDA or MPI programming. The toolbox lets you use parallel-enabled functions in MATLAB and other toolboxes. You can use the toolbox with Simulink® to run multiple simulations of a model in parallel. Programs and models can run in both interactive and batch modes.
  • Partial Differential Equation Toolbox™
  • ROS Toolbox
  • Robotics Toolbox™ - holonomic vehicle. The Toolbox also including a detailed Simulink model for a quadrotor flying robot.
  • Deep Learning Toolbox™ - term memory (LSTM) networks to perform classification and regression on image, time-series, and text data. You can build network architectures such as generative adversarial networks (GANs) and Siamese networks using automatic differentiation, custom training loops, and shared weights. With the Deep Network Designer app, you can design, analyze, and train networks graphically. It can exchange models with TensorFlow™ and PyTorch through the ONNX format and import models from TensorFlow-Keras and Caffe. The toolbox supports transfer learning with DarkNet-53, ResNet-50, NASNet, SqueezeNet and many other pretrained models.
  • Reinforcement Learning Toolbox™ - making algorithms for complex applications such as resource allocation, robotics, and autonomous systems.
  • Deep Learning HDL Toolbox™ - built bitstreams for running a variety of deep learning networks on supported Xilinx® and Intel® FPGA and SoC devices. Profiling and estimation tools let you customize a deep learning network by exploring design, performance, and resource utilization tradeoffs.
  • Model Predictive Control Toolbox™ - loop simulations, you can evaluate controller performance.
  • Vision HDL Toolbox™ - streaming algorithms for the design and implementation of vision systems on FPGAs and ASICs. It provides a design framework that supports a diverse set of interface types, frame sizes, and frame rates. The image processing, video, and computer vision algorithms in the toolbox use an architecture appropriate for HDL implementations.
  • SoC Blockset™
  • Wireless HDL Toolbox™ - verified, hardware-ready Simulink® blocks and subsystems for developing 5G, LTE, and custom OFDM-based wireless communication applications. It includes reference applications, IP blocks, and gateways between frame and sample-based processing.
  • ThingSpeak™ - of-concept IoT systems that require analytics.
  • SEA-MAT
  • Gramm - level interface to produce publication-quality plots of complex data with varied statistical visualizations. Gramm is inspired by R's ggplot2 library.
  • hctsa - series analysis using Matlab.
  • Plotly
  • YALMIP
  • NVIDIA Developer CUDA
  • CUDA - accelerated applications, the sequential part of the workload runs on the CPU, which is optimized for single-threaded. The compute intensive portion of the application runs on thousands of GPU cores in parallel. When using CUDA, developers can program in popular languages such as C, C++, Fortran, Python and MATLAB.
  • CUDA Toolkit Documentation
  • CUDA Quick Start Guide
  • CUDA on WSL
  • CUDA GPU support for TensorFlow
  • NVIDIA Deep Learning cuDNN Documentation
  • NVIDIA GPU Cloud Documentation
  • NVIDIA NGC - optimized software for deep learning, machine learning, and high-performance computing (HPC) workloads.
  • NVIDIA NGC Containers - accelerated software for AI, machine learning and HPC. These containers take full advantage of NVIDIA GPUs on-premises and in the cloud.
  • CUDA Toolkit - accelerated applications. The CUDA Toolkit allows you can develop, optimize, and deploy your applications on GPU-accelerated embedded systems, desktop workstations, enterprise data centers, cloud-based platforms and HPC supercomputers. The toolkit includes GPU-accelerated libraries, debugging and optimization tools, a C/C++ compiler, and a runtime library to build and deploy your application on major architectures including x86, Arm and POWER.
  • NVIDIA cuDNN - accelerated library of primitives for [deep neural networks](https://developer.nvidia.com/deep-learning). cuDNN provides highly tuned implementations for standard routines such as forward and backward convolution, pooling, normalization, and activation layers. cuDNN accelerates widely used deep learning frameworks, including [Caffe2](https://caffe2.ai/), [Chainer](https://chainer.org/), [Keras](https://keras.io/), [MATLAB](https://www.mathworks.com/solutions/deep-learning.html), [MxNet](https://mxnet.incubator.apache.org/), [PyTorch](https://pytorch.org/), and [TensorFlow](https://www.tensorflow.org/).
  • CUDA-X HPC - X HPC includes highly tuned kernels essential for high-performance computing (HPC).
  • NVIDIA Container Toolkit - container) and utilities to automatically configure containers to leverage NVIDIA GPUs.
  • Minkowski Engine - differentiation library for sparse tensors. It supports all standard neural network layers such as convolution, pooling, unpooling, and broadcasting operations for sparse tensors.
  • CUTLASS - performance matrix-multiplication (GEMM) at all levels and scales within CUDA. It incorporates strategies for hierarchical decomposition and data movement similar to those used to implement cuBLAS.
  • CUB
  • Tensorman
  • Numba - aware optimizing compiler for Python sponsored by Anaconda, Inc. It uses the LLVM compiler project to generate machine code from Python syntax. Numba can compile a large subset of numerically-focused Python, including many NumPy functions. Additionally, Numba has support for automatic parallelization of loops, generation of GPU-accelerated code, and creation of ufuncs and C callbacks.
  • Chainer - based deep learning framework aiming at flexibility. It provides automatic differentiation APIs based on the define-by-run approach (dynamic computational graphs) as well as object-oriented high-level APIs to build and train neural networks. It also supports CUDA/cuDNN using [CuPy](https://github.com/cupy/cupy) for high performance training and inference.
  • CuPy - compatible multi-dimensional array on CUDA. CuPy consists of the core multi-dimensional array class, cupy.ndarray, and many functions on it. It supports a subset of numpy.ndarray interface.
  • CatBoost
  • cuDF - like API that will be familiar to data engineers & data scientists, so they can use it to easily accelerate their workflows without going into the details of CUDA programming.
  • cuML - learn.
  • ArrayFire - purpose library that simplifies the process of developing software that targets parallel and massively-parallel architectures including CPUs, GPUs, and other hardware acceleration devices.
  • Thrust - level interface greatly enhances programmer productivity while enabling performance portability between GPUs and multicore CPUs.
  • AresDB - powered real-time analytics storage and query engine. It features low query latency, high data freshness and highly efficient in-memory and on disk storage management.
  • Arraymancer - dimensional array) project in Nim. The main focus is providing a fast and ergonomic CPU, Cuda and OpenCL ndarray library on which to build a scientific computing ecosystem.
  • Kintinuous - time dense visual SLAM system capable of producing high quality globally consistent point and mesh reconstructions over hundreds of metres in real-time with only a low-cost commodity RGB-D sensor.
  • GraphVite - speed and large-scale embedding learning in various applications.
  • Bioinformatics
  • European Bioinformatics Institute
  • National Center for Biotechnology Information
  • Online Courses in Bioinformatics |ISCB - International Society for Computational Biology
  • Bioinformatics | Coursera
  • Top Bioinformatics Courses | Udemy
  • Biometrics Courses | Udemy
  • Learn Bioinformatics with Online Courses and Lessons | edX
  • Bioinformatics Graduate Certificate | Harvard Extension School
  • Bioinformatics and Biostatistics | UC San Diego Extension
  • Bioinformatics and Proteomics - Free Online Course Materials | MIT
  • Introduction to Biometrics course - Biometrics Institute
  • Bioconductor - throughput genomic data. Bioconductor uses the [R statistical programming language](https://www.r-project.org/about.html), and is open source and open development. It has two releases each year, and an active user community. Bioconductor is also available as an [AMI (Amazon Machine Image)](https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/AMIs.html) and [Docker images](https://docs.docker.com/engine/reference/commandline/images/).
  • Bioconda
  • UniProt - quality and freely accessible set of protein sequences annotated with functional information.
  • Bowtie 2 - efficient tool for aligning sequencing reads to long reference sequences. It is particularly good at aligning reads of about 50 up to 100s or 1,000s of characters, and particularly good at aligning to relatively long (mammalian) genomes.
  • Biopython
  • BioRuby
  • BioJava
  • BioPHP
  • Avogadro - platform use in computational chemistry, molecular modeling, bioinformatics, materials science, and related areas. It offers flexible high quality rendering and a powerful plugin architecture.
  • Ascalaph Designer
  • Anduril - thoughput data in biomedical research, and the platform is fully extensible by third parties. Ready-made tools support data visualization, DNA/RNA/ChIP-sequencing, DNA/RNA microarrays, cytometry and image analysis.
  • Galaxy - based platform for accessible, reproducible, and transparent computational biomedical research. It allows users without programming experience to easily specify parameters and run individual tools as well as larger workflows. It also captures run information so that any user can repeat and understand a complete computational analysis.
  • PathVisio - source pathway analysis and drawing software which allows drawing, editing, and analyzing biological pathways. It is developed in Java and can be extended with plugins.
  • Orange
  • Basic Local Alignment Search Tool
  • OSIRIS - domain, free, and open source STR analysis software designed for clinical, forensic, and research use, and has been validated for use as an expert system for single-source samples.
  • NCBI BioSystems
  • Creative Commons Attribution 4.0 International (CC BY 4.0) Public License
Programming Languages
C++ 4 MATLAB 3 Nim 1 Python 1 Rust 1 Cuda 1 HTML 1
Keywords
cuda 8 gpu 5 nvidia 4 matlab 3 cpp 3 gpu-computing 2 machine-learning 2 deep-learning 2 nvidia-hpc-sdk 2 cxx20 2 cxx17 2 cxx14 2 cxx11 2 cxx 2 cpp20 2 cpp17 2 cpp14 2 cpp11 2 algorithms 2 tensor 2 color-scheme 1 color-theme 1 colours 1 matlab-schemes 1 matlab-syntax 1 scheme 1 syntax-highlighting 1 theme 1 matrix 1 matrix-completion 1 matrix-factorization 1 rpca 1 subspace-learning 1 deep-learning-library 1 subspace-tracking 1 tensor-decomposition 1 visualization 1 data-visualization 1 stats 1 plot 1 statistics 1 nvidia-docker 1 docker 1 slam 1 reconstruction 1 parallel-computing 1 openmp 1 opencl 1 nim 1 neural-networks 1