Run by grad students. We share summaries of interesting ML content (papers/blogs/videos), code snippets etc. #DeepLearning #MachineLearning #DataScience #python
Jun 7, 2021 • 6 tweets • 3 min read
There are way too many papers on #MachineLearning and on #DeepLearning these days. How to choose which papers to read? A tiny thread 🧵
The first one is our absolute favorite. Arxiv sanity by none other than @karpathy!
In Self-attention mechanism, we are updating the features of a given point, with respect to other features. The attention proposed in this paper is also known as Scaled dot-product attention.
Apr 7, 2021 • 8 tweets • 2 min read
This paper shares 56 stories of researchers in Computer Vision, young and old, scientists and engineers. Reading it was a cocktail of emotions as you simultaneously relate to the stories of joy,excitement,cynicism,and fear. Give it a read!
The authors have taken the Bert architecture and applied it on an images with minimal changes.Since the compute increases with the length of the sequence, instead of taking each pixel as a word, they propose to split the image into some ’N’ patches and take each of them as token.
Apr 6, 2021 • 5 tweets • 3 min read
Depending on the problem we are trying to solve, the loss function varies. Today we are going to learn about Triplet losses. You must have heard about it while reading about Siamese networks.
To get the intuition behind the Machine Learning algorithms, we need to have some background in Math, especially Linear Algebra, Probability & Calculus. Consolidating a few cheat-sheets here. A thread 👇
For Linear Algebra: Topics include Vector spaces, Matrix vector operations, Rank of a matrix, Norms, Eigenvectors and values and a bit of Matrix calculus too.