https://github.com/mikeroyal/neuromorphic-computing-guide

Learn about the Neumorphic engineering process of creating large-scale integration (VLSI) systems containing electronic analog circuits to mimic neuro-biological architectures.
https://github.com/mikeroyal/neuromorphic-computing-guide

artificial-neural-networks brain-imaging neocortex neural-engine neural-engineering neural-machine-translation neural-network neural-networks neural-processes neural-radiance-fields neural-rendering neural-simulators neuroimaging neuromorphic neuromorphic-computing neuromorphic-engineering neuromorphic-hardware neuron-models neuronal-network neuroscience

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Learn about the Neumorphic engineering process of creating large-scale integration (VLSI) systems containing electronic analog circuits to mimic neuro-biological architectures.

Host: GitHub
URL: https://github.com/mikeroyal/neuromorphic-computing-guide
Owner: mikeroyal
Created: 2021-10-03T20:38:52.000Z (over 4 years ago)
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Last Pushed: 2024-01-04T22:47:02.000Z (over 2 years ago)
Last Synced: 2025-03-29T19:01:46.253Z (about 1 year ago)
Topics: artificial-neural-networks, brain-imaging, neocortex, neural-engine, neural-engineering, neural-machine-translation, neural-network, neural-networks, neural-processes, neural-radiance-fields, neural-rendering, neural-simulators, neuroimaging, neuromorphic, neuromorphic-computing, neuromorphic-engineering, neuromorphic-hardware, neuron-models, neuronal-network, neuroscience
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- Readme: README.md
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Neuromorphic Computing Guide

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#### A guide covering Neuromorphic Computing including the applications, libraries and tools that will make you better and more efficient with Neuromorphic Computing development.

**Note: You can easily convert this markdown file to a PDF in [VSCode](https://code.visualstudio.com/) using this handy extension [Markdown PDF](https://marketplace.visualstudio.com/items?itemName=yzane.markdown-pdf).** Also, checkout the mdBook version [Neuromorphic Computing Guide mdBook (Special thanks to jonathanwoollett-light)](https://jonathanwoollett-light.github.io/Neuromorphic-Computing-Guide/book/index.html).

Types of Neural Networks

# Table of Contents

1. [Getting Stat\rted with Neuromorphic Computing](https://github.com/mikeroyal/Neuromorphic-Computing-Guide#Getting-Started-with-Neuromorphic-Computing)
* [Developer Resources](https://github.com/mikeroyal/Neuromorphic-Computing-Guide#developer-resources)
* [Online Training Courses](https://github.com/mikeroyal/Neuromorphic-Computing-Guide#online-training-courses)
* [Books](https://github.com/mikeroyal/Neuromorphic-Computing-Guide#books)
* [YouTube videos](https://github.com/mikeroyal/Neuromorphic-Computing-Guide#youtube-videos)

2. [Neuromorphic Computing Tools, Libraries, and Frameworks](https://github.com/mikeroyal/Neuromorphic-Computing-Guide#Neuromorphic-Computing-tools-libraries-and-frameworks)

3. [Algorithms](https://github.com/mikeroyal/Neuromorphic-Computing-Guide#Algorithms)

4. [Machine Learning](https://github.com/mikeroyal/Neuromorphic-Computing-Guide#machine-learning)

5. [Deep Learning Development](https://github.com/mikeroyal/Neuromorphic-Computing-Guide#Deep-Learning-Development)

6. [Reinforcement Learning Development](https://github.com/mikeroyal/Neuromorphic-Computing-Guide#Reinforcement-Learning-Development)

7. [Computer Vision Development](https://github.com/mikeroyal/Neuromorphic-Computing-Guide#computer-vision-development)

8. [Natural Language Processing (NLP) Development](https://github.com/mikeroyal/Neuromorphic-Computing-Guide#nlp-development)

9. [Bioinformatics](https://github.com/mikeroyal/Neuromorphic-Computing-Guide#bioinformatics)

10. [Robotics Development](https://github.com/mikeroyal/Neuromorphic-Computing-Guide#robotics-development)

11. [Electric charge, field, and potential](https://github.com/mikeroyal/Neuromorphic-Computing-Guide#electric-charge-field-and-potential)

- Charge and electric force (Coulomb's law): Electric charge, field, and potential
- Electric field: Electric charge, field, and potential
- Electric potential energy, electric potential, and voltage: Electric charge, field, and potential

12. [Circuits](https://github.com/mikeroyal/Neuromorphic-Computing-Guide#circuits)

- Ohm's law and circuits with resistors: Circuits
- Circuits with capacitors: Circuits

13. [Magnetic forces, magnetic fields, and Faraday's law](https://github.com/mikeroyal/Neuromorphic-Computing-Guide#electromagnetic-waves-and-interference)

- Magnets and Magnetic Force: Magnetic forces, magnetic fields, and Faraday's law
- Magnetic field created by a current: Magnetic forces, magnetic fields, and Faraday's law
- Electric motors: Magnetic forces, magnetic fields, and Faraday's law
- Magnetic flux and Faraday's law

14. [Electromagnetic waves and interference](https://github.com/mikeroyal/Neuromorphic-Computing-Guide#electromagnetic-waves-and-interference)

- Introduction to electromagnetic waves: Electromagnetic waves and interference
- Interference of electromagnetic waves

15. [CUDA Development](https://github.com/mikeroyal/Neuromorphic-Computing-Guide#cuda-development)

16. [MATLAB Development](https://github.com/mikeroyal/Neuromorphic-Computing-Guide#matlab-development)

17. [Python Development](https://github.com/mikeroyal/Neuromorphic-Computing-Guide#python-development)

18. [C/C++ Development](https://github.com/mikeroyal/Neuromorphic-Computing-Guide#cc-development)

# Getting Started with Neuromorphic Computing
[Back to the Top](#table-of-contents)

[Neuromorphic Computing](https://en.wikipedia.org/wiki/Neuromorphic_engineering) is the use of [very large scale integration (VLSI)](https://en.wikipedia.org/wiki/Very-large-scale_integration) systems containing electronic [analog circuits](https://en.wikipedia.org/wiki/Analog_circuit) to simulate the neuro-biological architectures present in the human brain ad nervous system.

Intel Loihi 2, its second-generation neuromorphic research chip.

The Akida Neuromorphic System-on-Chip (NSoC) developed by BrainChip.

### Developer Resources

[Back to the Top](#table-of-contents)

* [Neuromorphic Computing - Next Generation of AI | Intel](https://www.intel.com/content/www/us/en/research/neuromorphic-computing.html)

* [Next-Level Neuromorphic Computing: Intel Lab's Loihi 2 Chip | Intel](https://www.intel.com/content/www/us/en/research/neuromorphic-computing-loihi-2-technology-brief.html)

* [Neuromorphic Computing | NIST](https://www.nist.gov/programs-projects/neuromorphic-computing)

* [Programming and Usability of Neuromorphic Computing | ORNL](https://www.ornl.gov/division/csmd/projects/programming-and-usability-neuromorphic-computing)

* [Neuromorphic Computing | EBRAINS Research Infrastructure](https://ebrains.eu/service/neuromorphic-computing/)

* [Neuromorphic devices & systems | IBM Research Zurich](https://www.zurich.ibm.com/st/neuromorphic/)

* [Light-Emitting Artificial Synapses for Neuromorphic Computing (Research Paper PDF)](https://spj.sciencemag.org/journals/research/2022/9786023/)

### Online Training Courses

[Back to the Top](#table-of-contents)

* [Top Computational Neuroscience Courses Online | Coursera](https://www.coursera.org/courses?query=computational%20neuroscience)

* [Computational Neuroscience Course Online | Coursera](https://www.coursera.org/learn/computational-neuroscience)

* [Computational Neuroscience: Neuronal Dynamics of Cognition Course Online | edX](https://www.edx.org/course/computational-neuroscience-neuronal-dynamics-of-co)

* [Fundamentals of Neuroscience, Part 1: The Electrical Properties of the Neuron | Harvard Online Learning](https://online-learning.harvard.edu/course/fundamentals-neuroscience-part-1-electrical-properties-neuron?delta=2)

* [Fundamentals of Neuroscience, Part 2: Neurons and Networks | Harvard Online Learning](https://online-learning.harvard.edu/course/fundamentals-neuroscience-part-2-neurons-and-networks?delta=2)

* [Fundamentals of Neuroscience, Part 3: The Brain | Harvard Online Learning](https://online-learning.harvard.edu/course/fundamentals-neuroscience-part-3-brain?delta=2)

* [Introduction to Computational Neuroscience | MIT OpenCourseWare](https://ocw.mit.edu/courses/brain-and-cognitive-sciences/9-29j-introduction-to-computational-neuroscience-spring-2004/)

* [Brain and Cognitive Sciences Online Course | MIT OpenCourseWare](https://ocw.mit.edu/courses/brain-and-cognitive-sciences/)

* [Getting Started with PyTorch](https://pytorch.org/get-started/locally/)

* [Top Pytorch Courses Online | Coursera](https://www.coursera.org/courses?query=pytorch&page=1)

* [Top Pytorch Courses Online | Udemy](https://www.udemy.com/topic/PyTorch/)

* [Learn PyTorch with Online Courses and Classes | edX](https://www.edx.org/learn/pytorch)

* [Intro to Deep Learning with PyTorch | Udacity](https://www.udacity.com/course/deep-learning-pytorch--ud188)

* [PyTorch on Azure - Deep Learning with PyTorch | Microsoft Azure](https://azure.microsoft.com/en-us/develop/pytorch/)

* [Deep Learning with PyTorch | Amazon Web Services (AWS)](https://aws.amazon.com/pytorch/)

* [Getting started with PyTorch on Google Cloud](https://cloud.google.com/ai-platform/training/docs/getting-started-pytorch)

### Books

[Back to the Top](#table-of-contents)

* [Neuromorphic Computing Principles and Organization by Abderazek Ben Abdallah and Khanh N. Dang](https://www.amazon.com/Neuromorphic-Computing-Principles-Organization-Abderazek-ebook-dp-B0B2VWTBJH/dp/B0B2VWTBJH/ref=mt_other?_encoding=UTF8&me=&qid=1664159825)

* [Neuromorphic Computing and Beyond: Parallel, Approximation, Near Memory, and Quantum by Khaled Salah Mohamed](https://www.amazon.com/Neuromorphic-Computing-Beyond-Parallel-Approximation/dp/3030372235)

* [Neuromorphic Engineering: The Scientist's, Algorithm Designer's, and Computer Architect's Perspectives on Brain-Inspired Computing by Elishai Ezra Tsur](https://www.amazon.com/Neuromorphic-Engineering-Scientists-Perspectives-Brain-Inspired/dp/036767680X/ref=sr_1_3?crid=1Q98YGBP8R3T5&keywords=Neuromorphic+Computing&qid=1664159919&qu=eyJxc2MiOiIzLjk0IiwicXNhIjoiMy42NSIsInFzcCI6IjIuNzUifQ%3D%3D&refinements=p_72%3A1250221011&rnid=1250219011&s=books&sprefix=neuromorphic+computing%2Cstripbooks%2C329&sr=1-3)

* [Memristors for Neuromorphic Circuits and Artificial Intelligence Applications by Jordi Suñé](https://www.amazon.com/Memristors-Neuromorphic-Artificial-Intelligence-Applications/dp/3039285769/ref=sr_1_2?crid=1Q98YGBP8R3T5&keywords=Neuromorphic+Computing&qid=1664159919&qu=eyJxc2MiOiIzLjk0IiwicXNhIjoiMy42NSIsInFzcCI6IjIuNzUifQ%3D%3D&refinements=p_72%3A1250221011&rnid=1250219011&s=books&sprefix=neuromorphic+computing%2Cstripbooks%2C329&sr=1-2)

* [Learning in Energy-Efficient Neuromorphic Computing: Algorithm and Architecture Co-Design by Nan Zheng, Pinaki Mazumder](https://www.google.com/books/edition/Learning_in_Energy_Efficient_Neuromorphi/IvC0DwAAQBAJ?hl=en&gbpv=0)

* [Neuromorphic Devices for Brain-inspired Computing: Artificial Intelligence, Perception, and Robotics by Qing Wan, Yi Shi](https://www.google.com/books/edition/Neuromorphic_Devices_for_Brain_inspired/GLtREAAAQBAJ?hl=en&gbpv=0)

* [Understanding and Bridging the Gap between Neuromorphic Computing and Machine Learning by Huajin Tang, Kaushik Roy, Lei Deng](https://www.google.com/books/edition/Understanding_and_Bridging_the_Gap_betwe/mrIsEAAAQBAJ?hl=en&gbpv=0)

* [Photo-Electroactive Non-Volatile Memories for Data Storage and Neuromorphic Computing by Ye Zhou, Suting Han](https://www.google.com/books/edition/Photo_Electroactive_Non_Volatile_Memorie/03rnDwAAQBAJ?hl=en)

* [Neuromorphic Photonics by Bhavin J. Shastri, Paul R. Prucnal](https://www.google.com/books/edition/Neuromorphic_Photonics/VbvODgAAQBAJ?hl=en&gbpv=0)

### YouTube videos

[Back to the Top](#table-of-contents)

* [Neuromorphic Computing Explained | Jeffrey Shainline and Lex Fridman](https://www.youtube.com/watch?v=u22-2CTErIQ)

* [Brains Behind the Brains: Mike Davies and Neuromorphic Computing at Intel Labs | Intel](https://www.youtube.com/watch?v=GN3eSMoJcM8)

* [How Neuromorphic Computing Uses the Human Brain as a Model | Intel Labs](https://www.youtube.com/watch?v=Z1GdHNwQtt4)

* [ESWEEK 2021 Education - Introduction to Neuromorphic Computing](https://www.youtube.com/watch?v=gzD3ygkoJKE)

* [Stanford Seminar: Neuromorphic Chips: Addressing the Nanostransistor Challenge](https://www.youtube.com/watch?v=vHlbC74RJGU)

* [Brain-Like (Neuromorphic) Computing - Computerphile](https://www.youtube.com/watch?v=Qow8pIvExH4)

* [Photonic Neuromorphic Computing: The Future of AI? | ExplainingComputers ](https://www.youtube.com/watch?v=hBFLeQlG2og)

* [Machine learning + neuroscience = biologically feasible computing | Benjamin Migliori | TEDxSanDiego](https://www.youtube.com/watch?v=iPdKMs9cEAs)

# Neuromorphic Computing Tools, Libraries, and Frameworks
[Back to the Top](https://github.com/mikeroyal/Neuromorphic-Computing-Guide#table-of-contents)

[Lava](https://github.com/lava-nc/lava) is an open-source software framework for developing neuro-inspired applications and mapping them to neuromorphic hardware. Lava provides developers with the tools and abstractions to develop applications that fully exploit the principles of neural computation. Constrained in this way, like the brain, Lava applications allow neuromorphic platforms to intelligently process, learn from, and respond to real-world data with great gains in energy efficiency and speed compared to conventional computer architectures.

[Lava DL](https://github.com/lava-nc/lava-dl) is an enhanced version of [SLAYER](https://github.com/bamsumit/slayerPytorch). Some enhancements include support for recurrent network structures, a wider variety of neuron models and synaptic connections (complete list of features [here](https://github.com/lava-nc/lava-dl/blob/main/lib/dl/slayer/README.md)). This version of SLAYER is built on top of the PyTorch deep learning framework, similar to its predecessor.

[Lava Dynamic Neural Fields (DNF)](https://github.com/lava-nc/lava-dnf) are neural attractor networks that generate stabilized activity patterns in recurrently connected populations of neurons. These activity patterns form the basis of neural representations, decision making, working memory, and learning. DNFs are the fundamental building block of [dynamic field theory](https://dynamicfieldtheory.org/), a mathematical and conceptual framework for modeling cognitive processes in a closed behavioral loop.

[Neuromorphic Constraint Optimization](https://github.com/lava-nc/lava-optimization) is a library of solvers that leverage neuromorphic hardware for constrained optimization. Constrained optimization searches for the values of input variables that minimize or maximize a given objective function, while the variables are subject to constraints. This kind of problem is ubiquitous throughout scientific domains and industries. Constrained optimization is a promising application for neuromorphic computing as it [naturally aligns with the dynamics of spiking neural networks](https://doi.org/10.1109/JPROC.2021.3067593).

[NeuroKit2](https://github.com/neuropsychology/NeuroKit) is a user-friendly Python package providing easy access to advanced biosignal processing routines.

[Neuro Digital Signal Processing(NeuroDSP) Toolbox](https://github.com/neurodsp-tools/neurodsp) is a collection of approaches for applying digital signal processing to neural time series, including algorithms that have been proposed for the analysis of neural time series. It also includes simulation tools for generating plausible simulations of neural time series.

[Norse](https://norse.github.io/norse/) is a deep learning tool with spiking neural networks (SNNs) in PyTorch. It expands PyTorch with primitives for bio-inspired neural components, bringing you two advantages: a modern and proven infrastructure based on PyTorch and deep learning-compatible spiking neural network components.

[JAX](https://github.com/google/jax) is [Autograd](https://github.com/hips/autograd) and [XLA](https://www.tensorflow.org/xla), brought together for high-performance numerical computing and machine learning research. It provides composable transformations of Python+NumPy programs such as differentiate, vectorize, parallelize, Just-In-Time (JIT) compile to GPU/TPU, and more.

[XLA (Accelerated Linear Algebra)](https://www.tensorflow.org/xla) is a domain-specific compiler for linear algebra that can accelerate TensorFlow models with potentially no source code changes.

[MNE-Python](https://github.com/mne-tools/mne-python) is an open-source Python package for exploring, visualizing, and analyzing human neurophysiological data such as Magnetoencephalography (MEG) , Electroencephalography (EEG), sEEG, ECoG, and more. It includes modules for data input/output, preprocessing, visualization, source estimation, time-frequency analysis, connectivity analysis, machine learning, and statistics.

[Nengo](https://github.com/nengo/nengo) is a Python library for building and simulating large-scale neural models. It can create sophisticated spiking and non-spiking neural simulations with sensible defaults in a few lines of code.

[Keras](https://keras.io) is a high-level neural networks API, written in Python and capable of running on top of TensorFlow, CNTK, or Theano.It was developed with a focus on enabling fast experimentation. It is capable of running on top of TensorFlow, Microsoft Cognitive Toolkit, R, Theano, or PlaidML.

[ONNX Runtime](https://github.com/microsoft/onnxruntime) is a cross-platform, high performance ML inferencing and training accelerator. It supports models from deep learning frameworks such as PyTorch and TensorFlow/Keras as well as classical machine learning libraries such as scikit-learn, LightGBM, XGBoost, etc.

[TorchScript](https://pytorch.org/docs/stable/jit.html) is a way to create serializable and optimizable models from PyTorch code. This allows any TorchScript program to be saved from a Python process and loaded in a process where there is no Python dependency.

[TorchServe](https://pytorch.org/serve/) is a flexible and easy to use tool for serving PyTorch models.

[TensorFlow Model Optimization Toolkit](https://www.tensorflow.org/model_optimization) is a suite of tools that users, both novice and advanced, can use to optimize machine learning models for deployment and execution. It provides supported techniques that include quantization and pruning for sparse weights. Along with APIs built specifically for Keras.

[DeepSpars](https://github.com/neuralmagic/deepsparse) is an inference runtime offering GPU-class performance on CPUs and APIs to integrate ML into your application.

[Intel® Neural Compressor](https://github.com/intel/neural-compressor) is a Low Precision Optimization Tool, targeting to provide unified APIs for network compression technologies, such as low precision quantization, sparsity, pruning, knowledge distillation, across different deep learning frameworks to pursue optimal inference performance.

[Kornia](https://kornia.github.io/) is a differentiable computer vision library that consists of a set of routines and differentiable modules to solve generic CV (Computer Vision) problems.

[PyTorch-NLP](https://pytorchnlp.readthedocs.io/en/latest/) is a library for Natural Language Processing (NLP) in Python. It’s built with the very latest research in mind, and was designed from day one to support rapid prototyping. PyTorch-NLP comes with pre-trained embeddings, samplers, dataset loaders, metrics, neural network modules and text encoders.

[Ignite](https://pytorch.org/ignite) is a high-level library to help with training and evaluating neural networks in PyTorch flexibly and transparently.

[Hummingbird](https://github.com/microsoft/hummingbird) is a library for compiling trained traditional ML models into tensor computations. It allows users to seamlessly leverage neural network frameworks (such as PyTorch) to accelerate traditional ML models.

[Deep Graph Library (DGL)](https://www.dgl.ai/) is a Python package built for easy implementation of graph neural network model family, on top of PyTorch and other frameworks.

[TensorLy](http://tensorly.org/stable/home.html) is a high level API for tensor methods and deep tensorized neural networks in Python that aims to make tensor learning simple.

[GPyTorch](https://cornellius-gp.github.io/) is a Gaussian process library implemented using PyTorch, designed for creating scalable, flexible Gaussian process models.

[Poutyne](https://poutyne.org/) is a Keras-like framework for PyTorch and handles much of the boilerplating code needed to train neural networks.

[Forte](https://github.com/asyml/forte/tree/master/docs) is a toolkit for building NLP pipelines featuring composable components, convenient data interfaces, and cross-task interaction.

[TorchMetrics](https://github.com/PyTorchLightning/metrics) is a Machine learning metrics for distributed, scalable PyTorch applications.

[Captum](https://captum.ai/) is an open source, extensible library for model interpretability built on PyTorch.

[Transformer](https://github.com/huggingface/transformers) is a State-of-the-art Natural Language Processing for Pytorch, TensorFlow, and JAX.

[Hydra](https://hydra.cc) is a framework for elegantly configuring complex applications.

[Accelerate](https://huggingface.co/docs/accelerate) is a simple way to train and use PyTorch models with multi-GPU, TPU, mixed-precision.

[Ray](https://github.com/ray-project/ray) is a fast and simple framework for building and running distributed applications.

[ParlAI](http://parl.ai/) is a unified platform for sharing, training, and evaluating dialog models across many tasks.

[PyTorchVideo](https://pytorchvideo.org/) is a deep learning library for video understanding research. Hosts various video-focused models, datasets, training pipelines and more.

[Opacus](https://opacus.ai/) is a library that enables training PyTorch models with Differential Privacy.

[PyTorch Lightning](https://github.com/williamFalcon/pytorch-lightning) is a Keras-like ML library for PyTorch. It leaves core training and validation logic to you and automates the rest.

[PyTorch Geometric Temporal](https://github.com/benedekrozemberczki/pytorch_geometric_temporal) is a temporal (dynamic) extension library for PyTorch Geometric.

[PyTorch Geometric](https://github.com/rusty1s/pytorch_geometric) is a library for deep learning on irregular input data such as graphs, point clouds, and manifolds.

[Raster Vision](https://docs.rastervision.io/) is an open source framework for deep learning on satellite and aerial imagery.

[CrypTen](https://github.com/facebookresearch/CrypTen) is a framework for Privacy Preserving ML. Its goal is to make secure computing techniques accessible to ML practitioners.

[Optuna](https://optuna.org/) is an open source hyperparameter optimization framework to automate hyperparameter search.

[Pyro](http://pyro.ai/) is a universal probabilistic programming language (PPL) written in Python and supported by PyTorch on the backend.

[Albumentations](https://github.com/albu/albumentations) is a fast and extensible image augmentation library for different CV tasks like classification, segmentation, object detection and pose estimation.

[Skorch](https://github.com/skorch-dev/skorch) is a high-level library for PyTorch that provides full scikit-learn compatibility.

[MMF](https://mmf.sh/) is a modular framework for vision & language multimodal research from Facebook AI Research (FAIR).

[AdaptDL](https://github.com/petuum/adaptdl) is a resource-adaptive deep learning training and scheduling framework.

[Polyaxon](https://github.com/polyaxon/polyaxon) is a platform for building, training, and monitoring large-scale deep learning applications.

[TextBrewer](http://textbrewer.hfl-rc.com/) is a PyTorch-based knowledge distillation toolkit for natural language processing

[AdverTorch](https://github.com/BorealisAI/advertorch) is a toolbox for adversarial robustness research. It contains modules for generating adversarial examples and defending against attacks.

[NeMo](https://github.com/NVIDIA/NeMo) is a a toolkit for conversational AI.

[ClinicaDL](https://clinicadl.readthedocs.io/) is a framework for reproducible classification of Alzheimer's Disease

[Stable Baselines3 (SB3)](https://github.com/DLR-RM/stable-baselines3) is a set of reliable implementations of reinforcement learning algorithms in PyTorch.

[TorchIO](https://github.com/fepegar/torchio) is a set of tools to efficiently read, preprocess, sample, augment, and write 3D medical images in deep learning applications written in PyTorch.

[PySyft](https://github.com/OpenMined/PySyft) is a Python library for encrypted, privacy preserving deep learning.

[Flair](https://github.com/flairNLP/flair) is a very simple framework for state-of-the-art natural language processing (NLP).

[Glow](https://github.com/pytorch/glow) is a ML compiler that accelerates the performance of deep learning frameworks on different hardware platforms.

[FairScale](https://github.com/facebookresearch/fairscale) is a PyTorch extension library for high performance and large scale training on one or multiple machines/nodes.

[MONAI](https://monai.io/) is a deep learning framework that provides domain-optimized foundational capabilities for developing healthcare imaging training workflows.

[PFRL](https://github.com/pfnet/pfrl) is a deep reinforcement learning library that implements various state-of-the-art deep reinforcement algorithms in Python using PyTorch.

[Einops](https://github.com/arogozhnikov/einops) is a flexible and powerful tensor operations for readable and reliable code.

[PyTorch3D](https://pytorch3d.org/) is a deep learning library that provides efficient, reusable components for 3D Computer Vision research with PyTorch.

[Ensemble Pytorch](https://ensemble-pytorch.readthedocs.io/) is a unified ensemble framework for PyTorch to improve the performance and robustness of your deep learning model.

[Lightly](https://github.com/lightly-ai/lightly) is a computer vision framework for self-supervised learning.

[Higher](https://github.com/facebookresearch/higher) is a library which facilitates the implementation of arbitrarily complex gradient-based meta-learning algorithms and nested optimisation loops with near-vanilla PyTorch.

[Horovod](http://horovod.ai/) is a distributed training library for deep learning frameworks. Horovod aims to make distributed DL fast and easy to use.

[PennyLane](https://pennylane.ai/) is a library for quantum ML, automatic differentiation, and optimization of hybrid quantum-classical computations.

[Detectron2](https://github.com/facebookresearch/detectron2) is FAIR's next-generation platform for object detection and segmentation.

[Fastai](https://docs.fast.ai/) is a library that simplifies training fast and accurate neural nets using modern best practices.

# Algorithms
[Back to the Top](https://github.com/mikeroyal/Neuromorphic-Computing-Guide#table-of-contents)

[Fuzzy logic](https://www.investopedia.com/terms/f/fuzzy-logic.asp) is a heuristic approach that allows for more advanced decision-tree processing and better integration with rules-based programming.

**Architecture of a Fuzzy Logic System. Source: [ResearchGate](https://www.researchgate.net/figure/Architecture-of-a-fuzzy-logic-system_fig2_309452475)**

[Support Vector Machine (SVM)](https://web.stanford.edu/~hastie/MOOC-Slides/svm.pdf) is a supervised machine learning model that uses classification algorithms for two-group classification problems.

**Support Vector Machine (SVM). Source:[OpenClipArt](https://openclipart.org/detail/182977/svm-support-vector-machines)**

[Neural networks](https://www.ibm.com/cloud/learn/neural-networks) are a subset of machine learning and are at the heart of deep learning algorithms. The name/structure is inspired by the human brain copying the process that biological neurons/nodes signal to one another.

**Deep neural network. Source: [IBM](https://www.ibm.com/cloud/learn/neural-networks)**

[Convolutional Neural Networks (R-CNN)](https://stanford.edu/~shervine/teaching/cs-230/cheatsheet-convolutional-neural-networks) is an object detection algorithm that first segments the image to find potential relevant bounding boxes and then run the detection algorithm to find most probable objects in those bounding boxes.

**Convolutional Neural Networks. Source:[CS231n](https://cs231n.github.io/convolutional-networks/#conv)**

[Recurrent neural networks (RNNs)](https://www.ibm.com/cloud/learn/recurrent-neural-networks) is a type of artificial neural network which uses sequential data or time series data.

**Recurrent Neural Networks. Source: [Slideteam](https://www.slideteam.net/recurrent-neural-networks-rnns-ppt-powerpoint-presentation-file-templates.html)**

[Multilayer Perceptrons (MLPs)](https://deepai.org/machine-learning-glossary-and-terms/multilayer-perceptron) is multi-layer neural networks composed of multiple layers of [perceptrons](https://en.wikipedia.org/wiki/Perceptron) with a threshold activation.

**Multilayer Perceptrons. Source: [DeepAI](https://deepai.org/machine-learning-glossary-and-terms/multilayer-perceptron)**

[Random forest](https://www.ibm.com/cloud/learn/random-forest) is a commonly-used machine learning algorithm, which combines the output of multiple decision trees to reach a single result. A decision tree in a forest cannot be pruned for sampling and therefore, prediction selection. Its ease of use and flexibility have fueled its adoption, as it handles both classification and regression problems.

**Random forest. Source: [wikimedia](https://community.tibco.com/wiki/random-forest-template-tibco-spotfirer-wiki-page)**

[Decision trees](https://www.cs.cmu.edu/~bhiksha/courses/10-601/decisiontrees/) are tree-structured models for classification and regression.

***Decision Trees. Source: [CMU](http://www.cs.cmu.edu/~bhiksha/courses/10-601/decisiontrees/)*

[Naive Bayes](https://en.wikipedia.org/wiki/Naive_Bayes_classifier) is a machine learning algorithm that is used solved calssification problems. It's based on applying [Bayes' theorem](https://www.mathsisfun.com/data/bayes-theorem.html) with strong independence assumptions between the features.

**Bayes' theorem. Source:[mathisfun](https://www.mathsisfun.com/data/bayes-theorem.html)**

# Machine Learning
[Back to the Top](https://github.com/mikeroyal/Neuromorphic-Computing-Guide#table-of-contents)

## Learning Resources for ML

[Machine Learning](https://www.ibm.com/cloud/learn/machine-learning) is a branch of artificial intelligence (AI) focused on building apps using algorithms that learn from data models and improve their accuracy over time without needing to be programmed.

[Machine Learning by Stanford University from Coursera](https://www.coursera.org/learn/machine-learning)

[AWS Training and Certification for Machine Learning (ML) Courses](https://aws.amazon.com/training/learning-paths/machine-learning/)

[Machine Learning Scholarship Program for Microsoft Azure from Udacity](https://www.udacity.com/scholarships/machine-learning-scholarship-microsoft-azure)

[Microsoft Certified: Azure Data Scientist Associate](https://docs.microsoft.com/en-us/learn/certifications/azure-data-scientist)

[Microsoft Certified: Azure AI Engineer Associate](https://docs.microsoft.com/en-us/learn/certifications/azure-ai-engineer)

[Azure Machine Learning training and deployment](https://docs.microsoft.com/en-us/azure/devops/pipelines/targets/azure-machine-learning)

[Learning Machine learning and artificial intelligence from Google Cloud Training](https://cloud.google.com/training/machinelearning-ai)

[Machine Learning Crash Course for Google Cloud](https://developers.google.com/machine-learning/crash-course/)

[JupyterLab](https://jupyterlab.readthedocs.io/)

[Scheduling Jupyter notebooks on Amazon SageMaker ephemeral instances](https://aws.amazon.com/blogs/machine-learning/scheduling-jupyter-notebooks-on-sagemaker-ephemeral-instances/)

[How to run Jupyter Notebooks in your Azure Machine Learning workspace](https://docs.microsoft.com/en-us/azure/machine-learning/how-to-run-jupyter-notebooks)

[Machine Learning Courses Online from Udemy](https://www.udemy.com/topic/machine-learning/)

[Machine Learning Courses Online from Coursera](https://www.coursera.org/courses?query=machine%20learning&)

[Learn Machine Learning with Online Courses and Classes from edX](https://www.edx.org/learn/machine-learning)

## ML Frameworks, Libraries, and Tools

[TensorFlow](https://www.tensorflow.org) is an end-to-end open source platform for machine learning. It has a comprehensive, flexible ecosystem of tools, libraries and community resources that lets researchers push the state-of-the-art in ML and developers easily build and deploy ML powered applications.

[PyTorch](https://pytorch.org) is a library for deep learning on irregular input data such as graphs, point clouds, and manifolds. Primarily developed by Facebook's AI Research lab.

[Amazon SageMaker](https://aws.amazon.com/sagemaker/) is a fully managed service that provides every developer and data scientist with the ability to build, train, and deploy machine learning (ML) models quickly. SageMaker removes the heavy lifting from each step of the machine learning process to make it easier to develop high quality models.

[Azure Databricks](https://azure.microsoft.com/en-us/services/databricks/) is a fast and collaborative Apache Spark-based big data analytics service designed for data science and data engineering. Azure Databricks, sets up your Apache Spark environment in minutes, autoscale, and collaborate on shared projects in an interactive workspace. Azure Databricks supports Python, Scala, R, Java, and SQL, as well as data science frameworks and libraries including TensorFlow, PyTorch, and scikit-learn.

[Microsoft Cognitive Toolkit (CNTK)](https://docs.microsoft.com/en-us/cognitive-toolkit/) is an open-source toolkit for commercial-grade distributed deep learning. It describes neural networks as a series of computational steps via a directed graph. CNTK allows the user to easily realize and combine popular model types such as feed-forward DNNs, convolutional neural networks (CNNs) and recurrent neural networks (RNNs/LSTMs). CNTK implements stochastic gradient descent (SGD, error backpropagation) learning with automatic differentiation and parallelization across multiple GPUs and servers.

[Apple CoreML](https://developer.apple.com/documentation/coreml) is a framework that helps integrate machine learning models into your app. Core ML provides a unified representation for all models. Your app uses Core ML APIs and user data to make predictions, and to train or fine-tune models, all on the user's device. A model is the result of applying a machine learning algorithm to a set of training data. You use a model to make predictions based on new input data.

[Tensorflow_macOS](https://github.com/apple/tensorflow_macos) is a Mac-optimized version of TensorFlow and TensorFlow Addons for macOS 11.0+ accelerated using Apple's ML Compute framework.

[Apache OpenNLP](https://opennlp.apache.org/) is an open-source library for a machine learning based toolkit used in the processing of natural language text. It features an API for use cases like [Named Entity Recognition](https://en.wikipedia.org/wiki/Named-entity_recognition), [Sentence Detection](), [POS(Part-Of-Speech) tagging](https://en.wikipedia.org/wiki/Part-of-speech_tagging), [Tokenization](https://en.wikipedia.org/wiki/Tokenization_(data_security)) [Feature extraction](https://en.wikipedia.org/wiki/Feature_extraction), [Chunking](https://en.wikipedia.org/wiki/Chunking_(psychology)), [Parsing](https://en.wikipedia.org/wiki/Parsing), and [Coreference resolution](https://en.wikipedia.org/wiki/Coreference).

[Apache Airflow](https://airflow.apache.org) is an open-source workflow management platform created by the community to programmatically author, schedule and monitor workflows. Install. Principles. Scalable. Airflow has a modular architecture and uses a message queue to orchestrate an arbitrary number of workers. Airflow is ready to scale to infinity.

[Open Neural Network Exchange(ONNX)](https://github.com/onnx) is an open ecosystem that empowers AI developers to choose the right tools as their project evolves. ONNX provides an open source format for AI models, both deep learning and traditional ML. It defines an extensible computation graph model, as well as definitions of built-in operators and standard data types.

[Apache MXNet](https://mxnet.apache.org/) is a deep learning framework designed for both efficiency and flexibility. It allows you to mix symbolic and imperative programming to maximize efficiency and productivity. At its core, MXNet contains a dynamic dependency scheduler that automatically parallelizes both symbolic and imperative operations on the fly. A graph optimization layer on top of that makes symbolic execution fast and memory efficient. MXNet is portable and lightweight, scaling effectively to multiple GPUs and multiple machines. Support for Python, R, Julia, Scala, Go, Javascript and more.

[AutoGluon](https://autogluon.mxnet.io/index.html) is toolkit for Deep learning that automates machine learning tasks enabling you to easily achieve strong predictive performance in your applications. With just a few lines of code, you can train and deploy high-accuracy deep learning models on tabular, image, and text data.

[Anaconda](https://www.anaconda.com/) is a very popular Data Science platform for machine learning and deep learning that enables users to develop models, train them, and deploy them.

[PlaidML](https://github.com/plaidml/plaidml) is an advanced and portable tensor compiler for enabling deep learning on laptops, embedded devices, or other devices where the available computing hardware is not well supported or the available software stack contains unpalatable license restrictions.

[OpenCV](https://opencv.org) is a highly optimized library with focus on real-time computer vision applications. The C++, Python, and Java interfaces support Linux, MacOS, Windows, iOS, and Android.

[Scikit-Learn](https://scikit-learn.org/stable/index.html) is a Python module for machine learning built on top of SciPy, NumPy, and matplotlib, making it easier to apply robust and simple implementations of many popular machine learning algorithms.

[Weka](https://www.cs.waikato.ac.nz/ml/weka/) is an open source machine learning software that can be accessed through a graphical user interface, standard terminal applications, or a Java API. It is widely used for teaching, research, and industrial applications, contains a plethora of built-in tools for standard machine learning tasks, and additionally gives transparent access to well-known toolboxes such as scikit-learn, R, and Deeplearning4j.

[Caffe](https://github.com/BVLC/caffe) is a deep learning framework made with expression, speed, and modularity in mind. It is developed by Berkeley AI Research (BAIR)/The Berkeley Vision and Learning Center (BVLC) and community contributors.

[Theano](https://github.com/Theano/Theano) is a Python library that allows you to define, optimize, and evaluate mathematical expressions involving multi-dimensional arrays efficiently including tight integration with NumPy.

[nGraph](https://github.com/NervanaSystems/ngraph) is an open source C++ library, compiler and runtime for Deep Learning. The nGraph Compiler aims to accelerate developing AI workloads using any deep learning framework and deploying to a variety of hardware targets.It provides the freedom, performance, and ease-of-use to AI developers.

[NVIDIA cuDNN](https://developer.nvidia.com/cudnn) is a GPU-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/).

[Jupyter Notebook](https://jupyter.org/) is an open-source web application that allows you to create and share documents that contain live code, equations, visualizations and narrative text. Jupyter is used widely in industries that do data cleaning and transformation, numerical simulation, statistical modeling, data visualization, data science, and machine learning.

[Apache Spark](https://spark.apache.org/) is a unified analytics engine for large-scale data processing. It provides high-level APIs in Scala, Java, Python, and R, and an optimized engine that supports general computation graphs for data analysis. It also supports a rich set of higher-level tools including Spark SQL for SQL and DataFrames, MLlib for machine learning, GraphX for graph processing, and Structured Streaming for stream processing.

[Apache Spark Connector for SQL Server and Azure SQL](https://github.com/microsoft/sql-spark-connector) is a high-performance connector that enables you to use transactional data in big data analytics and persists results for ad-hoc queries or reporting. The connector allows you to use any SQL database, on-premises or in the cloud, as an input data source or output data sink for Spark jobs.

[Apache PredictionIO](https://predictionio.apache.org/) is an open source machine learning framework for developers, data scientists, and end users. It supports event collection, deployment of algorithms, evaluation, querying predictive results via REST APIs. It is based on scalable open source services like Hadoop, HBase (and other DBs), Elasticsearch, Spark and implements what is called a Lambda Architecture.

[Cluster Manager for Apache Kafka(CMAK)](https://github.com/yahoo/CMAK) is a tool for managing [Apache Kafka](https://kafka.apache.org/) clusters.

[BigDL](https://bigdl-project.github.io/) is a distributed deep learning library for Apache Spark. With BigDL, users can write their deep learning applications as standard Spark programs, which can directly run on top of existing Spark or Hadoop clusters.

[Eclipse Deeplearning4J (DL4J)](https://deeplearning4j.konduit.ai/) is a set of projects intended to support all the needs of a JVM-based(Scala, Kotlin, Clojure, and Groovy) deep learning application. This means starting with the raw data, loading and preprocessing it from wherever and whatever format it is in to building and tuning a wide variety of simple and complex deep learning networks.

[Tensorman](https://github.com/pop-os/tensorman) is a utility for easy management of Tensorflow containers by developed by [System76]( https://system76.com).Tensorman allows Tensorflow to operate in an isolated environment that is contained from the rest of the system. This virtual environment can operate independent of the base system, allowing you to use any version of Tensorflow on any version of a Linux distribution that supports the Docker runtime.

[Numba](https://github.com/numba/numba) is an open source, NumPy-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](https://chainer.org/) is a Python-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.

[XGBoost](https://xgboost.readthedocs.io/) is an optimized distributed gradient boosting library designed to be highly efficient, flexible and portable. It implements machine learning algorithms under the Gradient Boosting framework. XGBoost provides a parallel tree boosting (also known as GBDT, GBM) that solve many data science problems in a fast and accurate way. It supports distributed training on multiple machines, including AWS, GCE, Azure, and Yarn clusters. Also, it can be integrated with Flink, Spark and other cloud dataflow systems.

[cuML](https://github.com/rapidsai/cuml) is a suite of libraries that implement machine learning algorithms and mathematical primitives functions that share compatible APIs with other RAPIDS projects. cuML enables data scientists, researchers, and software engineers to run traditional tabular ML tasks on GPUs without going into the details of CUDA programming. In most cases, cuML's Python API matches the API from scikit-learn.

# Deep Learning Development
[Back to the Top](https://github.com/mikeroyal/Neuromorphic-Computing-Guide#table-of-contents)

## Deep Learning Learning Resources

[Deep Learning](https://www.ibm.com/cloud/learn/deep-learning) is a subset of machine learning, which is essentially a neural network with three or more layers. These neural networks attempt to simulate the behavior of the human brain,though, far from matching its ability. This allows the neural networks to "learn" from large amounts of data. The Learning can be [supervised](https://en.wikipedia.org/wiki/Supervised_learning), [semi-supervised](https://en.wikipedia.org/wiki/Semi-supervised_learning) or [unsupervised](https://en.wikipedia.org/wiki/Unsupervised_learning).

[Deep Learning Online Courses | NVIDIA](https://www.nvidia.com/en-us/training/online/)

[Top Deep Learning Courses Online | Coursera](https://www.coursera.org/courses?query=deep%20learning)

[Top Deep Learning Courses Online | Udemy](https://www.udemy.com/topic/deep-learning/)

[Learn Deep Learning with Online Courses and Lessons | edX](https://www.edx.org/learn/deep-learning)

[Deep Learning Online Course Nanodegree | Udacity](https://www.udacity.com/course/deep-learning-nanodegree--nd101)

[Machine Learning Course by Andrew Ng | Coursera](https://www.coursera.org/learn/machine-learning?)

[Machine Learning Engineering for Production (MLOps) course by Andrew Ng | Coursera](https://www.coursera.org/specializations/machine-learning-engineering-for-production-mlops)

[Data Science: Deep Learning and Neural Networks in Python | Udemy](https://www.udemy.com/course/data-science-deep-learning-in-python/)

[Understanding Machine Learning with Python | Pluralsight ](https://www.pluralsight.com/courses/python-understanding-machine-learning)

[How to Think About Machine Learning Algorithms | Pluralsight](https://www.pluralsight.com/courses/machine-learning-algorithms)

[Deep Learning Courses | Stanford Online](https://online.stanford.edu/courses/cs230-deep-learning)

[Deep Learning - UW Professional & Continuing Education](https://www.pce.uw.edu/courses/deep-learning)

[Deep Learning Online Courses | Harvard University](https://online-learning.harvard.edu/course/deep-learning-0)

[Machine Learning for Everyone Courses | DataCamp](https://www.datacamp.com/courses/introduction-to-machine-learning-with-r)

[Artificial Intelligence Expert Course: Platinum Edition | Udemy](https://www.udemy.com/course/artificial-intelligence-exposed-future-10-extreme-edition/)

[Top Artificial Intelligence Courses Online | Coursera](https://www.coursera.org/courses?query=artificial%20intelligence)

[Learn Artificial Intelligence with Online Courses and Lessons | edX](https://www.edx.org/learn/artificial-intelligence)

[Professional Certificate in Computer Science for Artificial Intelligence | edX](https://www.edx.org/professional-certificate/harvardx-computer-science-for-artifical-intelligence)

[Artificial Intelligence Nanodegree program](https://www.udacity.com/course/ai-artificial-intelligence-nanodegree--nd898)

[Artificial Intelligence (AI) Online Courses | Udacity](https://www.udacity.com/school-of-ai)

[Intro to Artificial Intelligence Course | Udacity](https://www.udacity.com/course/intro-to-artificial-intelligence--cs271)

[Edge AI for IoT Developers Course | Udacity](https://www.udacity.com/course/intel-edge-ai-for-iot-developers-nanodegree--nd131)

[Reasoning: Goal Trees and Rule-Based Expert Systems | MIT OpenCourseWare](https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-034-artificial-intelligence-fall-2010/lecture-videos/lecture-3-reasoning-goal-trees-and-rule-based-expert-systems/)

[Expert Systems and Applied Artificial Intelligence](https://www.umsl.edu/~joshik/msis480/chapt11.htm)

[Autonomous Systems - Microsoft AI](https://www.microsoft.com/en-us/ai/autonomous-systems)

[Introduction to Microsoft Project Bonsai](https://docs.microsoft.com/en-us/learn/autonomous-systems/intro-to-project-bonsai/)

[Machine teaching with the Microsoft Autonomous Systems platform](https://docs.microsoft.com/en-us/azure/architecture/solution-ideas/articles/autonomous-systems)

[Autonomous Maritime Systems Training | AMC Search](https://www.amcsearch.com.au/ams-training)

[Top Autonomous Cars Courses Online | Udemy](https://www.udemy.com/topic/autonomous-cars/)

[Applied Control Systems 1: autonomous cars: Math + PID + MPC | Udemy](https://www.udemy.com/course/applied-systems-control-for-engineers-modelling-pid-mpc/)

[Learn Autonomous Robotics with Online Courses and Lessons | edX](https://www.edx.org/learn/autonomous-robotics)

[Artificial Intelligence Nanodegree program](https://www.udacity.com/course/ai-artificial-intelligence-nanodegree--nd898)

[Autonomous Systems Online Courses & Programs | Udacity](https://www.udacity.com/school-of-autonomous-systems)

[Edge AI for IoT Developers Course | Udacity](https://www.udacity.com/course/intel-edge-ai-for-iot-developers-nanodegree--nd131)

[Autonomous Systems MOOC and Free Online Courses | MOOC List](https://www.mooc-list.com/tags/autonomous-systems)

[Robotics and Autonomous Systems Graduate Program | Standford Online](https://online.stanford.edu/programs/robotics-and-autonomous-systems-graduate-program)

[Mobile Autonomous Systems Laboratory | MIT OpenCourseWare](https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-186-mobile-autonomous-systems-laboratory-january-iap-2005/lecture-notes/)

## Deep Learning Tools, Libraries, and Frameworks

[NVIDIA DLSS (Deep Learning Super Sampling)](https://developer.nvidia.com/dlss) is a temporal image upscaling AI rendering technology that increases graphics performance using dedicated Tensor Core AI processors on GeForce RTX™ GPUs. DLSS uses the power of a deep learning neural network to boost frame rates and generate beautiful, sharp images for your games.

[AMD FidelityFX Super Resolution (FSR)](https://www.amd.com/en/technologies/radeon-software-fidelityfx) is an open source, high-quality solution for producing high resolution frames from lower resolution inputs. It uses a collection of cutting-edge Deep Learning algorithms with a particular emphasis on creating high-quality edges, giving large performance improvements compared to rendering at native resolution directly. FSR enables “practical performance” for costly render operations, such as hardware ray tracing for the AMD RDNA™ and AMD RDNA™ 2 architectures.

[Intel Xe Super Sampling (XeSS)](https://www.youtube.com/watch?v=Y9hfpf-SqEg) is a temporal image upscaling AI rendering technology that increases graphics performance similar to NVIDIA's [DLSS (Deep Learning Super Sampling)](https://developer.nvidia.com/dlss). Intel's Arc GPU architecture (early 2022) will have GPUs that feature dedicated Xe-cores to run XeSS. The GPUs will have Xe Matrix eXtenstions matrix (XMX) engines for hardware-accelerated AI processing. XeSS will be able to run on devices without XMX, including integrated graphics, though, the performance of XeSS will be lower on non-Intel graphics cards because it will be powered by [DP4a instruction](https://www.intel.com/content/dam/www/public/us/en/documents/reference-guides/11th-gen-quick-reference-guide.pdf).

[Cluster Manager for Apache Kafka(CMAK)](https://github.com/yahoo/CMAK) is a tool for managing [Apache Kafka](https://kafka.apache.org/) clusters.

[Deep Learning Toolbox™](https://www.mathworks.com/products/deep-learning.html) is a tool that provides a framework for designing and implementing deep neural networks with algorithms, pretrained models, and apps. You can use convolutional neural networks (ConvNets, CNNs) and long short-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™](https://www.mathworks.com/products/reinforcement-learning.html) is a tool that provides an app, functions, and a Simulink® block for training policies using reinforcement learning algorithms, including DQN, PPO, SAC, and DDPG. You can use these policies to implement controllers and decision-making algorithms for complex applications such as resource allocation, robotics, and autonomous systems.

[Deep Learning HDL Toolbox™](https://www.mathworks.com/products/deep-learning-hdl.html) is a tool that provides functions and tools to prototype and implement deep learning networks on FPGAs and SoCs. It provides pre-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™](https://www.mathworks.com/products/matlab-parallel-server.html) is a tool that lets you solve computationally and data-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.

[LIBSVM](https://www.csie.ntu.edu.tw/~cjlin/libsvm/) is an integrated software for support vector classification, (C-SVC, nu-SVC), regression (epsilon-SVR, nu-SVR) and distribution estimation (one-class SVM). It supports multi-class classification.

[Scikit-Learn](https://scikit-learn.org/stable/index.html) is a simple and efficient tool for data mining and data analysis. It is built on NumPy,SciPy, and mathplotlib.

[PyTorch](https://pytorch.org) is a library for deep learning on irregular input data such as graphs, point clouds, and manifolds. Primarily developed by Facebook's AI Research lab.

[Tensorflow_macOS](https://github.com/apple/tensorflow_macos) is a Mac-optimized version of TensorFlow and TensorFlow Addons for macOS 11.0+ accelerated using Apple's ML Compute framework.

[Anaconda](https://www.anaconda.com/) is a very popular Data Science platform for machine learning and deep learning that enables users to develop models, train them, and deploy them.

[OpenCV](https://opencv.org) is a highly optimized library with focus on real-time computer vision applications. The C++, Python, and Java interfaces support Linux, MacOS, Windows, iOS, and Android.

[Microsoft Project Bonsai](https://azure.microsoft.com/en-us/services/project-bonsai/) is a low-code AI platform that speeds AI-powered automation development and part of the Autonomous Systems suite from Microsoft. Bonsai is used to build AI components that can provide operator guidance or make independent decisions to optimize process variables, improve production efficiency, and reduce downtime.

[Microsoft AirSim](https://microsoft.github.io/AirSim/lidar.html) is a simulator for drones, cars and more, built on Unreal Engine (with an experimental Unity release). AirSim is open-source, cross platform, and supports [software-in-the-loop simulation](https://www.mathworks.com/help///ecoder/software-in-the-loop-sil-simulation.html) with popular flight controllers such as PX4 & ArduPilot and [hardware-in-loop](https://www.ni.com/en-us/innovations/white-papers/17/what-is-hardware-in-the-loop-.html) with PX4 for physically and visually realistic simulations. It is developed as an Unreal plugin that can simply be dropped into any Unreal environment. AirSim is being developed as a platform for AI research to experiment with deep learning, computer vision and reinforcement learning algorithms for autonomous vehicles.

[CARLA](https://github.com/carla-simulator/carla) is an open-source simulator for autonomous driving research. CARLA has been developed from the ground up to support development, training, and validation of autonomous driving systems. In addition to open-source code and protocols, CARLA provides open digital assets (urban layouts, buildings, vehicles) that were created for this purpose and can be used freely.

[ROS/ROS2 bridge for CARLA(package)](https://github.com/carla-simulator/ros-bridge) is a bridge that enables two-way communication between ROS and CARLA. The information from the CARLA server is translated to ROS topics. In the same way, the messages sent between nodes in ROS get translated to commands to be applied in CARLA.

[ROS Toolbox](https://www.mathworks.com/products/ros.html) is a tool that provides an interface connecting MATLAB® and Simulink® with the Robot Operating System (ROS and ROS 2), enabling you to create a network of ROS nodes. The toolbox includes MATLAB functions and Simulink blocks to import, analyze, and play back ROS data recorded in rosbag files. You can also connect to a live ROS network to access ROS messages.

[Robotics Toolbox™](https://www.mathworks.com/products/robotics.html) provides a toolbox that brings robotics specific functionality(designing, simulating, and testing manipulators, mobile robots, and humanoid robots) to MATLAB, exploiting the native capabilities of MATLAB (linear algebra, portability, graphics). The toolbox also supports mobile robots with functions for robot motion models (bicycle), path planning algorithms (bug, distance transform, D*, PRM), kinodynamic planning (lattice, RRT), localization (EKF, particle filter), map building (EKF) and simultaneous localization and mapping (EKF), and a Simulink model a of non-holonomic vehicle. The Toolbox also including a detailed Simulink model for a quadrotor flying robot.

[Image Processing Toolbox™](https://www.mathworks.com/products/image.html) is a tool that provides a comprehensive set of reference-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™](https://www.mathworks.com/products/computer-vision.html) is a tool that provides algorithms, functions, and apps for designing and testing computer vision, 3D vision, and video processing systems. You can perform object detection and tracking, as well as feature detection, extraction, and matching. You can automate calibration workflows for single, stereo, and fisheye cameras. For 3D vision, the toolbox supports visual and point cloud SLAM, stereo vision, structure from motion, and point cloud processing.

[Robotics Toolbox™](https://www.mathworks.com/products/robotics.html) is a tool that provides a toolbox that brings robotics specific functionality(designing, simulating, and testing manipulators, mobile robots, and humanoid robots) to MATLAB, exploiting the native capabilities of MATLAB (linear algebra, portability, graphics). The toolbox also supports mobile robots with functions for robot motion models (bicycle), path planning algorithms (bug, distance transform, D*, PRM), kinodynamic planning (lattice, RRT), localization (EKF, particle filter), map building (EKF) and simultaneous localization and mapping (EKF), and a Simulink model a of non-holonomic vehicle. The Toolbox also including a detailed Simulink model for a quadrotor flying robot.

[Model Predictive Control Toolbox™](https://www.mathworks.com/products/model-predictive-control.html) is a tool that provides functions, an app, and Simulink® blocks for designing and simulating controllers using linear and nonlinear model predictive control (MPC). The toolbox lets you specify plant and disturbance models, horizons, constraints, and weights. By running closed-loop simulations, you can evaluate controller performance.

[Predictive Maintenance Toolbox™](https://www.mathworks.com/products/predictive-maintenance.html) is a tool that lets you manage sensor data, design condition indicators, and estimate the remaining useful life (RUL) of a machine. The toolbox provides functions and an interactive app for exploring, extracting, and ranking features using data-based and model-based techniques, including statistical, spectral, and time-series analysis.

[Vision HDL Toolbox™](https://www.mathworks.com/products/vision-hdl.html) is a tool that provides pixel-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.

[Automated Driving Toolbox™](https://www.mathworks.com/products/automated-driving.html) is a MATLAB tool that provides algorithms and tools for designing, simulating, and testing ADAS and autonomous driving systems. You can design and test vision and lidar perception systems, as well as sensor fusion, path planning, and vehicle controllers. Visualization tools include a bird’s-eye-view plot and scope for sensor coverage, detections and tracks, and displays for video, lidar, and maps. The toolbox lets you import and work with HERE HD Live Map data and OpenDRIVE® road networks. It also provides reference application examples for common ADAS and automated driving features, including FCW, AEB, ACC, LKA, and parking valet. The toolbox supports C/C++ code generation for rapid prototyping and HIL testing, with support for sensor fusion, tracking, path planning, and vehicle controller algorithms.

[UAV Toolbox](https://www.mathworks.com/products/uav.html) is an application that provides tools and reference applications for designing, simulating, testing, and deploying unmanned aerial vehicle (UAV) and drone applications. You can design autonomous flight algorithms, UAV missions, and flight controllers. The Flight Log Analyzer app lets you interactively analyze 3D flight paths, telemetry information, and sensor readings from common flight log formats.

[Navigation Toolbox™](https://www.mathworks.com/products/navigation.html) is a tool that provides algorithms and analysis tools for motion planning, simultaneous localization and mapping (SLAM), and inertial navigation. The toolbox includes customizable search and sampling-based path planners, as well as metrics for validating and comparing paths. You can create 2D and 3D map representations, generate maps using SLAM algorithms, and interactively visualize and debug map generation with the SLAM map builder app.

[Lidar Toolbox™](https://www.mathworks.com/products/lidar.html) is a tool that provides algorithms, functions, and apps for designing, analyzing, and testing lidar processing systems. You can perform object detection and tracking, semantic segmentation, shape fitting, lidar registration, and obstacle detection. Lidar Toolbox supports lidar-camera cross calibration for workflows that combine computer vision and lidar processing.

[Mapping Toolbox™](https://www.mathworks.com/products/mapping.html) is a tool that provides algorithms and functions for transforming geographic data and creating map displays. You can visualize your data in a geographic context, build map displays from more than 60 map projections, and transform data from a variety of sources into a consistent geographic coordinate system.

# Reinforcement Learning Development
[Back to the Top](https://github.com/mikeroyal/Neuromorphic-Computing-Guide#table-of-contents)

## Reinforcement Learning Learning Resources

[Reinforcement Learning](https://www.ibm.com/cloud/learn/deep-learning#toc-deep-learn-md_Q_Of3) is a subset of machine learning, which is a neural network with three or more layers. These neural networks attempt to simulate the behavior of the human brain,though, far from matching its ability. This allows the neural networks to "learn" from a process in which a model learns to become more accurate for performing an action in an environment based on feedback in order to maximize the reward. The Learning can be [supervised](https://en.wikipedia.org/wiki/Supervised_learning), [semi-supervised](https://en.wikipedia.org/wiki/Semi-supervised_learning) or [unsupervised](https://en.wikipedia.org/wiki/Unsupervised_learning).

[Top Reinforcement Learning Courses | Coursera](https://www.coursera.org/courses?query=reinforcement%20learning)

[Top Reinforcement Learning Courses | Udemy](https://www.udemy.com/topic/reinforcement-learning/)

[Top Reinforcement Learning Courses | Udacity](https://www.udacity.com/course/reinforcement-learning--ud600)

[Reinforcement Learning Courses | Stanford Online](https://online.stanford.edu/courses/xcs234-reinforcement-learning)