50+ days of Machine Learning

 Hello :) Today is Day 156! A quick summary of today: finished and submitted the last assignment of the Stanford XCS224W: ML with graphs course read a few more papers about spatial-temporal graph neural networks As with the previous ones, I am not allowed to share anything related to the homework. All I will share is that I got the full score haha As for the new papers I read today.  I found about pytorch-geometric-temporal . It is an extension of the pytorch-geometric library that implements popular spatial-temporal GNN papers' architectures.  I feel like I do not want this blog to be filled with posts on the same topic 5 out of 7 days of the week (at least that is how I feel right now as I am doing literature review on spatial-temporal GNNs' application in intelligent transportation systems), so I will try to do some extra studying after my lab and share it here. I will still share what I do in the lab and what I study there, but it will be just a part of a blog post, r...

 Hello :) Today is Day 119! A quick summary of today: read Learning the Graphical Structure of Electronic Health Records with Graph Convolutional Transformer by Choi et al. (2020) saw: Four Ways of Thinking: Statistical, Interactive, Chaotic and Complex - David Sumpter ( youtube ) I learned about Graph Transformers on Day 110  and I had saved this paper from Professor Choi and wanted to read it for a while. Firstly, I wrote some summary notes of  it . One, it involves the usage of graphs, and two - Professor Edward Choi wrote it (I studied some of his lectures on  intro to AI ) Studying electronic healthcare records (EHR) using deep learning can help in various tasks, including predicting diagnosis, learning medical ceoncept representations, and making interpretable predictions. EHR data is often store as hierarchical graphs as in the picture. The common way to to process this data is to consider each encounter as an unordered set of features which does not...

 Hello :) Today is Day 115! A quick summary of today: submitted 3rd homework of XCS224W:ML with Graphs explored different huggingface capabilities with DeepLearning.AI As for the homework , we are not allowed to share anything from it. But I can happily share I got full marks ^^ As for the huggingface tutorial It showcased the different type of models that were available. Below is a summary.  Building a chat pipeline Text translation Text summarization Zero-shot audio classifier Apparently, the model seens the audio differently -  1 second of high resolution audio appears to the model as if it is 12 seconds of audio. Text to speech Object detection (code before the pic: od_pipe = pipeline("object-detection", "./models/facebook/detr-resnet-50")) We can use gradio as a sample interface I passed a picture of mine to check haha We can also get natural language descriptions Image captioning Example image Using the dog and woman pic again for multimodal QA Zero-shot image...