MATLAB-Guide

MATLAB Guide
https://github.com/mikeroyal/MATLAB-Guide

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Bioinformatics Learning Resources
Bioinformatics Tools, Libraries, and Frameworks
- 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
- 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.
- 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.
Computer Vision Learning Resources
Computer Vision Tools, Libraries, and Frameworks
- 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.
- 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.
- ROS Toolbox
- 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™
- 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.
- UAV Toolbox
- Mapping Toolbox™
- Statistics and Machine Learning Toolbox™
- Partial Differential Equation Toolbox™
- Data Acquisition Toolbox™
- 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.
CUDA Learning Resources
- 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
- NVIDIA Deep Learning cuDNN Documentation
- CUDA GPU support for TensorFlow
CUDA Tools Libraries, and Frameworks
- i. Basis
  - wikimedia
  - wikimedia
- iii. Dimension and Basis for Vector Spaces
  - sliderserve
- iii. Matrix-vector product
  - wikimedia
  - mathisfun
- iii. Transpose of a Matrix
  - slideserve
- ii. Matrix operations
  - DPS Concepts
  - SDSU Physics
- ii. Matrix representations of linear transformations
  - slideserve
- ii. Systems of equations as matrix equations
  - pinterest
  - mathisfun
- ii. Using elementary matrices
  - SDSU Physics
- i. Solving systems of equations
  - slideshare
  - slideshare
- i. Using row operations
  - YouTube
  - YouTube
- i. Vector operations
  - slideserve
  - pinterest
- iv. Linear transformations
  - Quora
  - slideserve
- iv. Row space, columns space, and rank of a matrix
  - slideshare
- v. Fundamental vector spaces
  - wikimedia
  - wolfram
- vi. Determinants
  - stackexchange
  - onlinemathlearning
- vii. Eigenvalues and eigenvectors
  - YouTube
- viii. Linear Regression
  - Medium
  - Fuzzy logic - tree processing and better integration with rules-based programming.
  - ResearchGate
  - Support Vector Machine (SVM) - group classification problems.
  - OpenClipArt
  - Convolutional Neural Networks (R-CNN)
  - CS231n
  - Slideteam
  - DeepAI
  - wikimedia
  - Decision trees - structured models for classification and regression.
  - CMU
  - Naive Bayes - theorem.html) with strong independence assumptions between the features.
  - mathisfun
  - Linear regression
  - Medium
  - wikimedia
  - Medium
  - wikimedia
  - Medium
  - wikimedia
  - Medium
  - wikimedia
  - Medium
  - wikimedia
  - Medium
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  - Medium
  - wikimedia
  - Medium
  - wikimedia
  - Medium
  - wikimedia
  - Medium
  - wikimedia
  - Medium
  - wikimedia
  - Medium
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  - Medium
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  - Medium
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  - Medium
  - Support Vector Machine (SVM) - group classification problems.
- - 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/).
  - 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.
  - 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.
  - CUDA-X HPC - X HPC includes highly tuned kernels essential for high-performance computing (HPC).
  - 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.
  - 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.
  - 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.
  - Linear Algebra - Online Courses | Harvard University
  - Linear Algebra | UC San Diego Extension
  - Linear Algebra for Machine Learning | UC San Diego Extension
  - Linear algebra
  - Linear Algebra | MIT Open Learning Library
  - Linear Algebra - Khan Academy
  - Mathematics for Machine Learning: Linear Algebra on Coursera
  - Top Linear Algebra Courses on Udemy
  - Learn Linear Algebra with Online Courses and Classes on edX
  - Linear Algebra in Twenty Five Lectures | UC Davis
  - Linear Algebra Resources | Dartmouth
  - 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.
  - cuML - learn.
  - NVIDIA Container Toolkit - container) and utilities to automatically configure containers to leverage NVIDIA GPUs.
  - 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

Programming Languages

C++ 19 Python 7 Scala 2 Shell 2 C# 2 Nim 1 MATLAB 1 TypeScript 1 Dockerfile 1 Java 1

ecosyste.ms

Data

Tools

Indexes

Applications

Experiments

Awesome

MATLAB-Guide

Bioinformatics Learning Resources

Bioinformatics Tools, Libraries, and Frameworks

Computer Vision Learning Resources

Computer Vision Tools, Libraries, and Frameworks

CUDA Learning Resources

CUDA Tools Libraries, and Frameworks

i. Basis

iii. Dimension and Basis for Vector Spaces

iii. Matrix-vector product

iii. Transpose of a Matrix

ii. Matrix operations

ii. Matrix representations of linear transformations

ii. Systems of equations as matrix equations

ii. Using elementary matrices

i. Solving systems of equations

i. Using row operations

i. Vector operations

iv. Linear transformations

iv. Row space, columns space, and rank of a matrix

v. Fundamental vector spaces

vi. Determinants

vii. Eigenvalues and eigenvectors

viii. Linear Regression