Library

Show and Tell! ML/AI Project Lightning Talks 1

In this session we invite any and all from expert to beginner to share their ML experience, knowledge and projects. Presentations will be 2 minutes, focused on your one-page-brief, followed by up to 10 minutes of open discussion. During discussion you should be prepared to share code if relevant but not run it. The goal is to provoke interesting project related discussion to help the presenting and audience learn and progress their knowledge and skills. Any and all ML topic areas are welcome. Thank you to our confirmed presenters: <strong>10/22 - Session #1</strong> <ul> <li><a href="<https://gist.github.com/gatorwatt/ef36c034a1831ca153f97b129d54adcc>" target="\_blank" rel="noopener noreferrer">Nicholas Teague - Automunge</a></li> <li><a href="<https://gist.github.com/mapmeld/0fce31bcad8a29376002cfa0d522836a>" target="\_blank" rel="noopener noreferrer">Nick Doiron - &nbsp;Measuring Gender Bias in Spanish Language Models</a></li> <li><a href="<https://gist.github.com/samadh192/0194072a90fe5e84abfe19864ef1b85f>">Abdul Samadh - Machine Learning for Encrypted Traffic Behavior Modelling</a>&nbsp;</li> <li><a href="<https://gist.github.com/KaiLicht/71e0d4a3279bec77bea2c67b22012cd0>">Kai Lichtenberg - Keyword Image Classification</a></li> <li><a href="<https://gist.github.com/shreesundara/b332c4bf39b0cd86933d64f4b59eb99d>">Shree Phani - Exploratory analysis of SVM support vectors</a></li> </ul> <strong>10/29 - Session #2</strong> <ul> <li><a href="<https://gist.github.com/The-Edgar/5d41315460d26fa45c0e11b52e51c29a>">Edgar Rootalu - Implementing Scalable Analytics Engineering Workflow with DBT (Data Build Tool)</a></li> <li><a href="<https://github.com/davijit868/PINN/blob/master/README.md>">Kamalkumar (Kamal) Rathinasamy - Visualize the Learning of Domain-Specific Knowledge in Neural Networks</a></li> <li><a href="<https://colab.research.google.com/drive/1kq_4FXa2-kgRo511fAemnX-nDQFFU63K?usp=sharing#scrollTo=0X9zl6Q1S3Wx>">Bryan Romas - Collaborative Filtering for Patient Recommendations</a></li> <li><a href="<https://docs.google.com/document/d/1fgjS8sdgfKnHTBmQKaMwZEWRRaE_Hc7zPDLU16j6Jc0/edit>">Fatma Elsafoury - Does BERT Pay Attention To Attribution</a></li> <li><a href="<https://gist.github.com/ankitsinha/59cbe79a9463bbf1c663efdda70d6c3b>">Ankit Sinha - Machine Learning for Routing Anamoly Detection</a></li> <li><a href="<https://gist.github.com/Ashnayak/51c16d9bb5e2490c87b4b9e1450c992f>">Ashwini Nayak &amp; Preethi Bharathy - Using NLP to explore the goldmine that is textual data: The Fundamentals of Text Summarization</a></li> <li><a href="<https://github.com/Nitinbharadwajm/FeatureEngineeringforUnsupervisedLearning/blob/main/Malware-Traffic-Detector.md>">Nitin Bharadwaj Mutukula - Encrypted Traffic Malware Presence Detector</a></li> </ul> Project can cover any area of ML practice such as: An interesting way you combined various ML methods into a POC or production solution An ML technique you recently research but perhaps have further questions about. CNNs, NLP, DNN, GANs, Clustering, Generative models, Data augmentation, Active learning, Transformers, Knowledge graphs, Rules based approaches or any ML topic of interest. The one-page-brief. Example - <https://colab.research.google.com/drive/13dI8PayD7Bpm4XNDVoOIxxhUt2giSz4O?usp=sharing> Try to make the projects brief easily shareable, ideally via a URL. These can be in the format of a text-only coolab, PDF slide, document, or perhaps a madewith.ml project.