🧵 Sensitivity vs Precision in Medical AI
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In the field of medical AI, sensitivity is a crucial performance measure. It indicates how effectively the model identifies individuals who actually have a specific disease. Essentially, it answers the question:

🧵Confusion Matrix:
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Classification models classify the samples into predefined categories. To evaluate how well these classifiers perform, we use confusion matrix. It is actually tabular way of visualizing and assessing model performance.

SmolVLM paper is out and it's packed with great findings on training a good smol vision LM!

Andi summarized them below, give it a read if you want to see more insights 🤠

Yıllarca github.com/acikkaynak ‘ta projeleri, sonra github.com/eser/topluluklar ‘la insanları bir araya getirme alışkanlığımla şimdi de BlueSky’da hesapları bir araya getirme inisiyatifi başlattım 😅 ben aklıma gelen isimleri ekledim. sizler de “beni de ekle” yazarsanız eklerim.

go.bsky.app/TLtndiT

Recurrent Neural Networks vs Transformers:
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- Core M...

Read more: https://longer.blue/posts/4FuiiTEfIK

Cancer Research
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I am proceeding cancer research repository to illuminate cancer drugs, structure of proteins, and protein-protein interaction networks. The following sections are ready for usage:

- 36 Different Cancer Drugs
- Protein Database with UniProt

github.com/GoktugGuverc...

🧵YOLO (You Only Look Once) was introduced by Joseph Redmon et al. in 2015 and has since undergone several iterations. The core idea behind YOLO is to perform object detection by treating it as a regression problem, where bounding box coordinates and class probabilities.

🧵 Understanding Vision Transformers
Before we jump into ViTs, let's quickly recap the basics. In traditional computer vision, Convolutional Neural Networks (CNNs) have been ruling the roost for image-related tasks.

🧵The Jacobian Matrix and Backpropagation
The Jacobian matrix represents the partial derivatives of a vector-valued function with respect to its input variables. In deep learning, it helps us compute the gradients of the loss function with respect to the network's parameters.

Self-supervised learning techniques construct and learn an embedding space by minimizing distances between embedding vectors of input images or just clustering them in a multidimensional graph.

Possible alternative to convolutional neural networks for visual recognition task is transformers. The strategy inspired by natural language processing to train visual transformers (ViT) is to pretrain on large amount of data, and finetune on target dataset.