{"id":15172864,"url":"https://github.com/rubenszimbres/repo-2018","last_synced_at":"2025-07-10T20:36:14.235Z","repository":{"id":107087256,"uuid":"114630397","full_name":"RubensZimbres/Repo-2018","owner":"RubensZimbres","description":"Deep Learning Summer School + Tensorflow + OpenCV cascade training + YOLO + COCO + CycleGAN + AWS EC2 Setup + AWS IoT Project + AWS SageMaker + AWS API Gateway + Raspberry Pi3 Ubuntu Core","archived":false,"fork":false,"pushed_at":"2021-12-06T22:14:48.000Z","size":121754,"stargazers_count":173,"open_issues_count":0,"forks_count":80,"subscribers_count":19,"default_branch":"master","last_synced_at":"2025-05-06T21:36:44.649Z","etag":null,"topics":["aws-ec2","aws-iot","aws-sagemaker","coco","computer-vision","cyclegan","ec2-instance","generative-adversarial-networks","haarcascade","iot","keras","opencv","opencv-library","opencv3","raspberry-pi","raspberry-pi-3","sagemaker","tensorflow","ubuntucore","yolo-model"],"latest_commit_sha":null,"homepage":"","language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/RubensZimbres.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2017-12-18T10:57:03.000Z","updated_at":"2024-11-12T02:17:04.000Z","dependencies_parsed_at":null,"dependency_job_id":"81642e1d-fcab-491b-ba66-5280c4099c85","html_url":"https://github.com/RubensZimbres/Repo-2018","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/RubensZimbres/Repo-2018","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/RubensZimbres%2FRepo-2018","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/RubensZimbres%2FRepo-2018/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/RubensZimbres%2FRepo-2018/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/RubensZimbres%2FRepo-2018/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/RubensZimbres","download_url":"https://codeload.github.com/RubensZimbres/Repo-2018/tar.gz/refs/heads/master","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/RubensZimbres%2FRepo-2018/sbom","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":264652968,"owners_count":23644363,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["aws-ec2","aws-iot","aws-sagemaker","coco","computer-vision","cyclegan","ec2-instance","generative-adversarial-networks","haarcascade","iot","keras","opencv","opencv-library","opencv3","raspberry-pi","raspberry-pi-3","sagemaker","tensorflow","ubuntucore","yolo-model"],"created_at":"2024-09-27T10:21:26.902Z","updated_at":"2025-07-10T20:36:14.161Z","avatar_url":"https://github.com/RubensZimbres.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Python Codes: IoT, Machine Learning, Computer Vision\n\nCodes in Machine Learning, Deep Learning, Reinforcement Learning, Artificial Intelligence and Computer Vision\n\n\u003cb\u003e Welcome to my GitHub repo. \u003c/b\u003e\n\nI am a Data Scientist and I code in Python. Here you will find some Machine Learning, Deep Learning, Natural Language Processing, Artificial Intelligence and Computer Vision models I developed.\n \nKeras version used in models: keras==2.0.8\nTensorflow version used in models: tensorflow==1.7.0  \n\n\u003cb\u003e AWS API Gateway \u003c/b\u003e  \n\nThis folder presents general guidelines to create an endpoint and API key in AWS API Gateway, that will receive data from a Python notebook sending sensor data, that arrives in API Gateway, is processed and cleaned in Lambda and is stored in DynamoDB. Data is then available for S3 to create a data pipeline and further visualization with Kibana or Quick Sight.\n\n\u003cb\u003e AWS SageMaker \u003c/b\u003e  \n\nThis sector presents the development of a Docker container with a customized Machine Learning model that is pushed into Elastic Container Registry and generates and endpoint in AWS SageMaker.\n\n\u003cb\u003e COCO Model \u003c/b\u003e\n\nCOCO (Common Objects in Context) is an image segmentation model. This folder presents my Pull Request regarding pycocotools library incompatibility between Python 2 and 3 suggesting a fix.  \n\n\u003cb\u003e CPU Temperature - IoT Project at AWS\u003c/b\u003e  \n\nIn this project I turned my notebook into an IoT device, where CPU temperature is collected via Linux command line run inside a Python notebook and then sent via MQTT protocol to Amazon AWS IoT service, integrated with DynamoDB, Data Pipeline, S3, Quick Sight and Cloud Watch. An alternative solution for near real-time data is also presented, using Kinesis, Firehose, Elasticsearch and Kibana.  \n\n\u003cb\u003e CycleGAN Project \u003c/b\u003e -  GANs \u003cbr/\u003e\n\nIn this folder I presente the steps to train a CycleGAN for Style Transfer applied to paintings of Vincent van Gogh and Pablo Picasso using an EC2 GPU Tesla K-80. Examples of outputs are presented.  \n\n\u003cb\u003e DEEP LEARNING SUMMER SCHOOL \u003c/b\u003e -  GANs \u003cbr/\u003e\n\nIn this folder you will find Tensorflow and Keras codes and also a Powerpoint presentation about GANs I developed for my lecture and workshop at the first Deep Learning Summer School in Brazil, at Goiás.  \n\n\u003cb\u003e EC2 INSTANCE SETUP \u003c/b\u003e\n\nA set up Manual I developed to create EC2 instances in Amazon AWS  \n\n\u003cb\u003e FLASK \u003c/b\u003e  \n\nThis folder contains the Python code for the REST API and also the request code.\n\n\u003cb\u003e KERAS \u003c/b\u003e\n \nGANs: Generative Adversarial Networks\n \nDCGANs: Deep Convolutional Generative Adversarial Networks  \n \n\u003cb\u003e NLP \u003c/b\u003e  \n\nNotebooks presenting Word2Vec similarities in trained Wikipedia corpus, Portuguese language.  \n\n\u003cb\u003e OPENCV \u003c/b\u003e\n \nIn this folder you will find a guide to create your own haarcascade.xml so that you can identify any object using OpenCV.  \n \n\u003cb\u003e Raspberry Pi3 - IoT Project\u003c/b\u003e  \n\nIn this project I use Raspberry Pi3 running in Ubuntu Core OS and attached a sensor and then send telemetry data to Amazon AWS IoT service, integrated with DynamoDB, S3, Quick Sight and Cloud Watch. I also plan to deploy a Deep Learning trained model into the device for classification purposes.  \n\n\u003cimg src=https://github.com/RubensZimbres/Repo-2018/raw/master/Raspberry%20Pi3%20IoT-Project/Pictures/small_kit.png\u003e  \n\n\u003cimg src=https://github.com/RubensZimbres/Repo-2018/blob/master/Raspberry%20Pi3%20IoT-Project/Pictures/sensors_dd.png\u003e  \n\n\u003cb\u003e TENSORFLOW \u003c/b\u003e \n \nModel for Regression\n \nModel for Classification \n \nLSTM: Long Short Term Memory Neural Networks\n \nGANs: Generative Adversarial Networks\n \nDCGANs: Deep Convolutional Generative Adversarial Networks\n \nInfoGANs \u003c/b\u003e Mutual Information Adversarial Networks where loss function is customized. \nMutual Info = H(B)-Sum(P(B=b).H(A|B=b)\n\nTensorboard: visualization of Tensorflow models' training  \n \n \n\u003cb\u003e SEG-NET \u003c/b\u003e : Image Segmentation with SEG-NET  \n \n\u003cb\u003e SMOTE \u003c/b\u003e : Synthetic Minority Oversampling Technique for imbalanced classes  \n\n\u003cb\u003e YOLO Model \u003c/b\u003e\n\nThe YOLO Model (You Only Look Once) is a Deep Learning project for Real-Time object detection. Examples of outputs are presented.\n\nDatasets available at Repo-2017  \n\n\u003cimg src=https://github.com/RubensZimbres/Repo-2018/raw/master/COCO%20model/Pictures/COCO_result_BIG.png\u003e\n \n\u003cimg src=https://github.com/RubensZimbres/Repo-2018/blob/master/Deep%20Learning%20Summer%20School/GANs.jpg\u003e\n \n \n\u003cimg src=https://github.com/RubensZimbres/Repo-2018/blob/master/Deep%20Learning%20Summer%20School/GAN_Best.PNG\u003e\n\n\u003cimg src=https://github.com/RubensZimbres/Repo-2018/blob/master/Deep%20Learning%20Summer%20School/TensorBoard_Loss.PNG\u003e\n\n\u003cimg src=https://github.com/RubensZimbres/Repo-2018/blob/master/Deep%20Learning%20Summer%20School/TensorBoard_Structure.PNG\u003e\n\n\u003cimg src=https://github.com/RubensZimbres/Repo-2018/raw/master/YOLO%20model/YOLO.png\u003e\n\n\u003cimg src=https://github.com/RubensZimbres/Repo-2018/blob/master/OpenCV/Training_Haar.png\u003e\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Frubenszimbres%2Frepo-2018","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Frubenszimbres%2Frepo-2018","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Frubenszimbres%2Frepo-2018/lists"}