395 skills found · Page 12 of 14
fabioharams / AzurestackScripts to deploy Azure Stack using small, personal machines
Narasimha1997 / StackMachineA simple educational virtual computer machine that can execute simple arithmetic and logical programs, This Virtual Machine has it's own memory model, instruction queue, virtual CPU and a compiler that comes with a parser.
solson / BorisA stack-based virtual machine written in C
stacky-language / StackyStacky is a simple stack-oriented programming language
oyavuzjr / Django Nextjs Ecommerce BoilerplateA full stack e-commerce web application created as a template for building analytics and machine learning tools in the admin interface.
Wiladams / AsimA Stack Intensive Machine
louisdorard / Full Stack MlFull-Stack Machine Learning Workshops
wlingze / Staaa stack-based virtual machine
andrade824 / 2D Graphics Accelerator IP2D Graphics Accelerator IP (with a full Linux software stack) used to create custom video game consoles and arcade machines
Chaitanyarai899 / Symphony WebandMLA full-stack project which utilizes Frontend and Backend development, spotify API integration along with Machine Learning to create an interactive Webapp for songs and playlist recommendations
anthonyfoust / AI Stack HomelabComplete AI automation stack optimized for Mac Mini M4, but can work in multiple machine configurations. Features n8n workflows, Ollama with Llama 3.2, Open WebUI, LiteLLM proxy, and MCP integration. Production-ready with automated backups, security, and family-safe configuration for you to learn more about AI at home.
Rishiraj8 / House Price PredictionA full-stack application for predicting house prices using a machine learning model. Features a React frontend for user interaction and a Flask backend powered by a trained Random Forest Regressor. Developed as part of a school friend's final-year project
Tech-with-Vidhya / Productionized Docker ML Model Application Into Kubernetes Cluster Using AWS EKS CloudFormation EMRThis project covers the end to end implementation of deploying and productionizing a dockerized/containerized machine learning python flask application into Kubernetes Cluster using the AWS Elastic Kubernetes Service (EKS), AWS Serverless Fargate Instances, AWS CloudFormation Cloud Stack and AWS Elastic Container Registry (ECR) Service. The machine learning business case implemented in this project includes a bank note authentication binary classifier model using Random Forest Classifier; which predicts and classifies a bank note either as a Fake Bank Note (Label 0) or a Genuine Bank Note (Label 1). Implementation Steps: 1. Creation of an end to end machine learning solution covering all the ML life-cycle steps of Data Exploration, Feature Selection, Model Training, Model Validation and Model Testing on the unseen production data. 2. Saved the finalised model as a pickle file. 3. Creation of a Python Flask based API; in order to render the ML model solution and inferences to the end-users. 4. Verified and tested the working status of the Python Flask API in the localhost set-up. 5. Creation of a Docker File (containing the steps/instructions to create a docker image) for the Python Flask based Bank Note Authentication Machine Learning Application embedded with Random Forest ML Classifier Model. 6. Creation of IAM Service Roles with appropriate policies to access the AWS Elastic Container Registry (ECR) Service and AWS Elastic Kubernetes Service (EKS) and AWS CloudFormation Service. 7. Created a new EC2 Linux Server Instance in AWS and copied the web application project’s directories and its files into the AWS Linux Server using SFTP linux commands. 8. Installed the Docker software and the supporting python libraries in the AWS EC2 Linux Server Instance; as per the “requirements.txt” file. 9. Transformation of the Docker File into a Docker Image and Docker Container representing the application; using docker build and run commands. 10. Creation of a Docker Repository within the AWS ECR Service and pushed the application docker image into the repository using AWS Command Line Interface (CLI) commands. 11. Creation of the Cloud Stack with private and public subnets using the AWS CloudFormation Service with appropriate IAM roles and policies. 12. Creation of the Kubernetes Cluster using the AWS EKS Service with appropriate IAM roles and policies and linked the cloud stack created using the AWS CloudFormation Service. 13. Creation of the AWS Serverless Fargate Profile and Fargate instances/nodes. 14. Creation and configured the “Deployment.yaml” and “Service.yaml” files using the Kubernetes kubectl commands. 15. Applied the “Deployment.yaml” with pods replica configuration to the AWS EKS Cluster Fargate Nodes; using the Kubernetes kubectl commands. 16. Applied the “Service.yaml” using the Kubernetes kubectl commands; to render and service the machine learning application to the end-users for public access with the creation of the production end-point. 17. Verified and tested the inferences of the productionized ML Application using the AWS Fargate end-point created in the AWS Kubernetes EKS Cluster. Tools & Technologies: Python, Flask, AWS, AWS EC2, Linux Server, Linux Commands, Command Line Interface (CLI), Docker, Docker Commands, AWS ECR, AWS IAM, AWS CloudFormation, AWS EKS, Kubernetes, Kubernetes kubectl Commands.
priscilla100 / Ensemble IDSThe exponential increase in the number of connected "things" and the proliferation in the usage of Internet of Things (IoT) devices has raised numerous challenges in terms of security, privacy, and interoperability. IoT devices are resource constrained in terms of computational power, onboard memory, network bandwidth, and energy availability which limits the implementation of cryptographic solutions. The heterogeneous nature of IoT devices makes them avenue for an attacker to exploit threats like spoofing, routing, MITM, and DoS attacks. With the current sophistication of threats IoT devices are subjected to, an Intrusion Detection System (IDS) is the preferred solution for IoT devices. An IDS continuously monitors incoming traffic, and analyzes it to detect possible signs of cyber threats. This research proposes a novel intelligent ensemble-based IDS that will reside in the IoT gateway. The uniqueness of our approach is to use an ensemble learning technique which combines multiple machine learning techniques in order to the improve the predictive performance and detection accuracy. Ensemble learning have been studied to increase the detection rate while obtaining better generalization performance due to the combination of several machine learning model also known as base learners. Three popularly known ensemble models (i.e. boosting, stacking, and voting) are used in evaluating the performance of our proposed IDS using three machine learning techniques: Decision Tree, Naive Bayes (NB), and k-Nearest Neighbor (KNN). Lastly, the proposed approach will be evaluated on two publicly available dataset; Intrusion Detection Evaluation Dataset (CIC-IDS2017) and N-BaIoT.
satyatumati / StackMachineInterpreterA stack machine interpreter using COOL language
4o4E / SlimefunStackingMachine粘液科技堆叠机器
omarsar / Helsinki MlMachine Learning in the Elastic Stack
adnanhf / Lung Sounds Classification SAE SVMStacked Autoencoder, Support Vector Machine, Wavelet Transform
dfa-ra / Stack MachineStack processor simulator with vector operations support
imr / Stack MachineSimple stack based microprocessor
