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https://github.com/mxagar/project_compilation

This repository compiles some of the projects I have worked on.
https://github.com/mxagar/project_compilation
computer-vision data-science deep-learning image-processing machine-learning portfolio robotics virtual-reality
Last synced: about 5 hours ago
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This repository compiles some of the projects I have worked on.
Host: GitHub
URL: https://github.com/mxagar/project_compilation
Owner: mxagar
Created: 2022-05-11T11:49:54.000Z (over 2 years ago)
Default Branch: main
Last Pushed: 2024-03-14T14:43:53.000Z (8 months ago)
Last Synced: 2024-04-24T12:14:21.581Z (7 months ago)
Topics: computer-vision, data-science, deep-learning, image-processing, machine-learning, portfolio, robotics, virtual-reality
Homepage:
Size: 9.54 MB
Stars: 1
Watchers: 1
Forks: 0
Open Issues: 0
Metadata Files:
- Readme: README.md
Awesome Lists containing this project

README

        # Mikel Sagardia - Project Portfolio

This repository compiles links to some of the projects I have worked on or I am currently working on:

- :mortar_board: [Some Public Research Projects](#some-public-research-projects)

- :soccer: [Some Side Projects](#some-side-projects)

- :books: [Some of My Guides on AI MOOCs and Books](#some-of-my-guides-on-ai-moocs-and-books)

- :mailbox: [Contact and Other Information](#contact-and-other-information)

## Some Public Research Projects

    

        Realtime Collision Avoidance for Robots with Arbitrary Geometries

    

    

        Video,

        Paper

    

    

        


            

            

            

        

    

    

        A Platform for Bimanual Virtual Assembly Training with Haptic Feedback in Large Multi-Object Environments

    

    

        Video,

        Paper

    

    

        


            

            

            

        

    

    

        VR-OOS: The DLR’s Virtual Reality Simulator for Telerobotic On-Orbit Servicing With Haptic Feedback

    

    

        Video,

        Paper

    

    

        


            

            

            

        

    

    

        Realtime Physics Simulations with Fast and Robust Collision Detection and Force Computation Integrated to Bullet

    

    

        Video,

        Paper

    

    

        


            

            

            

        

    

    

        Multimodal Evaluation of the Differences between Real and Virtual Assemblies

    

    

        Video,

        Paper

    

    

        


            

            

            

        

    

    

        Ultrapiano: A Novel Human-Machine Interface Applied to Virtual Reality

    

    

        Video,

        Paper

    

    

        


            

            

            

        

    

    

        Narrow Passage Sampling in the Observation of Robotic Assembly Tasks

    

    

        Paper

    

    

        


            

            

            

        

    

    

        My PhD Thesis: Virtual Manipulations with Force Feedback in Complex Interaction Scenarios

    

    

        Dissertation

    

    

        


            

            

            

        

    

## Some Side Projects

Please, note that these are some of my *side* projects, which might or might not be finished; in any case, the project status should be reported in each project page.

| Topic / Project | Link | Type of Data | Methods | Tools |

|---|---|---|---|---|

| Generating Image Vector Representations Using *SimCLR* | [Github](https://github.com/mxagar/simclr_pytorch_flowers) | Images :city_sunrise: | Contrastive Learning, CNN | Pytorch & Pytorch Lightning, Tensorboard |

| Face Generation with a Convolutional Generative Adversarial Network (GAN) | [Github](https://github.com/mxagar/face_generator_gan) | Images :city_sunrise: | GAN, CNN | Pytorch |

| Image Captioning: Image Description Text Generator Combining CNNs and RNNs | [Github](https://github.com/mxagar/image_captioning) | Images :city_sunrise:, Text :page_facing_up: | CNN, RNN, Image Captioning | Pytorch |

| Facial Keypoint Detection with Deep Convolutional Neural Networks (CNNs) | [Github](https://github.com/mxagar/P1_Facial_Keypoints) | Images :city_sunrise: | CNN, Regression | Pytorch |

| Skin Cancer Detection with Convolutional Neural Networks (CNNs) and T-SNE Visualization of Compressed Image Representations | [Github](https://github.com/mxagar/dermatologist-ai) | Images :city_sunrise: | CNN, Classification, Autoencoders, Manifold Learning | Pytorch, Scikit-Learn |

| Dog Breed Classification with Convolutional Neural Networks (CNNs) and Transfer Learning | [Github](https://github.com/mxagar/deep-learning-v2-pytorch/tree/master/project-dog-classification) | Images :city_sunrise: | CNN, Classification, Transfer Learning | Pytorch |

| American Sign Language (ASL) Image Analysis and Classification with Convolutional Neural Networks (CNNs) | [Github](https://github.com/mxagar/asl_alphabet_image_classification) | Images :city_sunrise: | CNN, Classification, Transfer Learning, Autoencoders | Tensorflow/Keras |

| A Satellite Image Processing Toolkit to Vectorize Water Bodies | [Github](https://github.com/mxagar/satellite_image_water_vectorizer) | Images :city_sunrise:, Geo-Spatial :satellite: :earth_africa: | Image Processing | Rasterio, GeoPandas, EarthPy, Matplotlib |

| Analysis and Modelling of the AirBnB Dataset from the Basque Country | [Blog](https://mikelsagardia.io/blog/airbnb-spain-basque-data-analysis.html), [Github](https://github.com/mxagar/airbnb_data_analysis) | Tabular :bar_chart:, Text :page_facing_up: | Regression, Classification, Hypothesis Testing | Scikit-Learn |

| A Template Package to Transform Machine Learning Research Notebooks into *Production-Level* Code and Its Application to Predicting Customer Churn | [Blog](https://mikelsagardia.io/blog/machine-learning-production-level.html), [Github](https://github.com/mxagar/customer_churn_production) | Tabular :bar_chart: | MLOps, Classification, Clean Code | Python Packaging, Scikit-Learn |

| A Boilerplate for Reproducible Machine Learning Pipelines with MLflow and Weights & Biases and Its Application to Song Genre Classification | [Github](https://github.com/mxagar/music_genre_classification) | Tabular :bar_chart: | MLOps, Classification, Random Forests | Scikit-Learn, MLflow, Weights & Biases |

| A Disaster Response Classification Web App with ETL and Machine Learning (ML) Pipelines | [Github](https://github.com/mxagar/disaster_response_pipeline) | Text :page_facing_up:, Tabular :bar_chart: | MLOps, Classification, Random Forests, CI | Scikit-Learn, NLTK, Flask, Pytest, Docker |

| A Reproducible Machine Learning Pipeline for Short-Term Rental Price Prediction in New York City | [Github](https://github.com/mxagar/ml_pipeline_rental_prices) | Tabular :bar_chart: | MLOps, Regression | Scikit-Learn, MLflow, Weights & Biases |

| Deployment of a Sentiment Analysis Recurrent Neural Network (RNN) Using AWS SageMaker | [Github](https://github.com/mxagar/sentiment_rnn_aws_deployment) | Text :page_facing_up: | MLOps, RNN, Classification, Sentiment Analysis | AWS SageMaker, API Gateway, Lambda, Pytorch |

| Deployment of a Census Salary Classification Model Using FastAPI | [Github](https://github.com/mxagar/census_model_deployment_fastapi) | Tabular :bar_chart: | MLOps, Classification, CI/CD, Deployment | Scikit-Learn, Python Packaging, FastAPI, Heroku, Pytest, Docker, AWS |

| A Dynamic Risk Assessment System: Monitoring of a Customer Churn Model | [Github](https://github.com/mxagar/churn_model_monitoring) | Tabular :bar_chart: | MLOps, Classification, Monitoring, Automation | Scikit-Learn, Flask, SQLite, SQLAlchemy |

| Deployment of a Personalized Online Course Recommender System Using Streamlit | [Github](https://github.com/mxagar/course_recommender_streamlit) | Tabular :bar_chart: | Recommender Systems, Unsupervised Learning, Regression, Classification, CI/CD, Deployment | Scikit-Learn, Tensorflow/Keras, Streamlit, Heroku, Pytest |

| Text Generation: TV Script Creation with a Recurrent Neural Network (RNN) | [Blog](https://mikelsagardia.io/blog/text-generation-rnn.html), [Github](https://github.com/mxagar/text_generator) | Text :page_facing_up: | RNN, Text Generation | Pytorch |

| Simultaneous Localization and Mapping (SLAM) in 2D Using a Graph-Based Approach | [Github](https://github.com/mxagar/slam_2d) | Tabular :bar_chart:, Spatio-Temporal  :robot: | SLAM | Numpy |

| Predicting Bike Sharing Patterns with Neural Networks Written from Scratch with Numpy | [Github](https://github.com/mxagar/deep-learning-v2-pytorch/tree/master/project-bikesharing) | Tabular :bar_chart: | MLP, Regression | Numpy |

| Analysis and Modelling of an Expert and Project Matching Dataset | [Github](https://github.com/mxagar/expert_chase) | Tabular :bar_chart: | EDA, Hypothesis Testing, Classification, Regression | Pandas, Scipy, Scikit-Learn, Matplotlib, etc. |

| A 80/20 Guide for Exploratory Data Analysis, Data Cleaning and Feature Engineering | [Blog](https://mikelsagardia.io/blog/data-processing-guide.html), [Github](https://github.com/mxagar/eda_fe_summary) | Tabular :bar_chart:, Text :page_facing_up: | *Guide*: EDA, Regression, Classification, Unsupervised Learning, Pipelines | Scikit-Learn, Pandas, Matplotlib, etc. |

| Beyond Image Classification: Object Detection and Semantic Segmentation Examples with Pytorch | [Github](https://github.com/mxagar/detection_segmentation_pytorch) | Images :city_sunrise: | *Compilation*: Object Detection & Segmentation | Pytorch |

| Text Sentiment Analysis: A Collection of Notes and Examples | [Github](https://github.com/mxagar/text_sentiment) | Text :page_facing_up: | *Compilation*: Sentiment Analysis | Pytorch |

## Some of My Guides on AI MOOCs and Books

This is a list of some repositories in which I collected notes for my future self while following courses/books. Note that in many cases the text is perfectly legible, but not edited; additionally, the original notes on a course/book might have been extended with other sources. For a complete list of the courses I have followed (with or without public notes), visit [my course compilation](https://github.com/mxagar/course_compilation).

| 
Course / Book + Link
  | My Notes | My Personal Rating |

| --- | --- | --- |

[Deep Learning Nanodegree, Udacity](https://www.udacity.com/course/deep-learning-nanodegree--nd101) | [Guide & Code](https://github.com/mxagar/deep_learning_udacity) | :star: :star: :star: :star: __ |

| [Computer Vision Nanodegree, Udacity](https://www.udacity.com/course/computer-vision-nanodegree--nd891) | [Guide & Code](https://github.com/mxagar/computer_vision_udacity) | :star: :star: :star: __ __ |

| [Machine Learning DevOps Engineer Nanodegree, Udacity](https://www.udacity.com/course/machine-learning-dev-ops-engineer-nanodegree--nd0821) | [Guide & Code](https://github.com/mxagar/mlops_udacity) | :star: :star: :star: :star: __ |

| [IBM Machine Learning Professional Certificate, Coursera / IBM](https://www.coursera.org/professional-certificates/ibm-machine-learning) | [Guide & Code](https://github.com/mxagar/machine_learning_ibm) | :star: :star: :star: :star: __ |

| [Machine Learning, Coursera / Univ. Standford](https://www.coursera.org/learn/machine-learning) | [Guide & Code](https://github.com/mxagar/machine_learning_coursera) | :star: :star: :star: :star: :star: |

| [Statistics with Python Specialization, Coursera / Uni. Michigan](https://www.coursera.org/specializations/statistics-with-python) | [Guide & Code](https://github.com/mxagar/statistics_with_python_coursera)  | :star: :star: :star: __ __ |

| [Data Science Nanodegree, Udacity](https://www.udacity.com/course/data-scientist-nanodegree--nd025) | [Guide & Code](https://github.com/mxagar/data_science_udacity) | :star: :star: :star: :star: __ |

| [Accelerated Computer Science Fundamentals Specialization, Coursera / Univ. Illinois](https://www.coursera.org/specializations/cs-fundamentals) | [Guide & Code](https://github.com/mxagar/accelerated_computer_science_coursera) | :star: :star: :star: __ __ |

| [Machine Vision: Theory and Applications, Steger et al.](https://www.amazon.com/Machine-Vision-Algorithms-Applications-Carsten/dp/3527413650) | [Guide & Code](https://github.com/mxagar/machine_vision_notes) | :star: :star: :star: :star: :star: |

| [Notes on Manipulation Robotics, Craig](https://www.amazon.com/Introduction-Robotics-Mechanics-Control-4th/dp/0133489795/ref=sr_1_1?crid=2Q39F2ZXG1D2W&keywords=Introduction+to+Robotics&qid=1664796958&qu=eyJxc2MiOiIyLjYyIiwicXNhIjoiMi42OCIsInFzcCI6IjMuMDkifQ%3D%3D&sprefix=introduction+to+robotics%2Caps%2C144&sr=8-1) | [Guide](https://github.com/mxagar/robotics_notes) | :star: :star: :star: :star: __ |

| [Natural Language Processing (NLP) Guide, Udemy](https://www.udemy.com/course/nlp-natural-language-processing-with-python/) | [Guide & Code](https://github.com/mxagar/nlp_guide) | :star: :star: :star: :star: __ |

| [Hyperparameter Optimization Guide, Udemy](https://www.udemy.com/course/hyperparameter-optimization-for-machine-learning/) | [Guide & Code](https://github.com/mxagar/hyperparameter-optimization) | :star: :star: :star: :star: __ |

| [Object Detection and Semantic Segmentation Guide, PyImageSearch](https://pyimagesearch.com/pyimagesearch-university/) | [Guide & Code](https://github.com/mxagar/detection_segmentation_pytorch) | :star: :star: :star: :star: __ |

| [Notes on Reinforcement Learning, Udemy](https://www.udemy.com/course/practical-ai-with-python-and-reinforcement-learning/) | [Guide & Code](https://github.com/mxagar/data_science_python_tools/blob/main/24_ReinforcementLearning/ReinforcementLearning_Guide.md) | :star: :star: :star: __ __ |

| [SQL Guide, Udemy](https://www.udemy.com/course/the-complete-sql-bootcamp/) | [Guide & Code](https://github.com/mxagar/sql_guide) | :star: :star: :star: :star: __ |

| Big Data and Spark Guide, [Udacity](https://www.udacity.com/course/learn-spark-at-udacity--ud2002) and [Datacamp](https://app.datacamp.com/learn/skill-tracks/big-data-with-pyspark) | [Guide & Code](https://github.com/mxagar/spark_big_data_guide) | :star: :star: :star: :star: __ |

| [Docker Guide, Udemy](https://www.udemy.com/course/docker-mastery/) | [Guide & Code](https://github.com/mxagar/templates/blob/master/docker_swarm_kubernetes/docker_swarm_kubernetes_howto.md) | :star: :star: :star: :star: __ |

| [Azure Guide (AZ900), Udemy](https://www.udemy.com/course/az900-azure/) | [Guide & Code](https://github.com/mxagar/azure_guide) | :star: :star: :star: :star: __ |

| [MLflow Guide, Udemy](https://www.udemy.com/course/mlflow-course) | [Guide & Code](https://github.com/mxagar/mlflow_guide) | :star: :star: :star: :star: __ |

| [Elasticseacrh Guide, Udemy](https://www.udemy.com/course/elasticsearch-complete-guide) | [Guide & Code](https://github.com/mxagar/elastic_search_guide) | :star: :star: :star: :star: __ |

## Contact and Other Information

For **professional collaboration**, you can find me at: [[email protected]](mailto:[email protected]).

For more information, you can visit:

- My **website/blog**: [https://mikelsagardia.io](https://mikelsagardia.io)

- The list of my **research papers**: [https://mikelsagardia.io/publications](https://mikelsagardia.io/publications)

- My **curriculum vitae**: [https://mikelsagardia.io/assets/MikelSagardia_CV.pdf](https://mikelsagardia.io/assets/MikelSagardia_CV.pdf)

- A list of **repositories on books and courses I have followed**: [https://github.com/mxagar/course_compilation](https://github.com/mxagar/course_compilation)