math

IntroductionOne of the perfect ways to deepen your understanding of the mathematics behind deep learning models and loss functions, and likewise an incredible strategy to improve your PyTorch skills is to get used to...

A few years ago, I stumbled across an interesting website called Project Euler. Named after one the very best mathematicians of all time Leonhard Euler, this site publishes math problems which can be designed...

“Causal reasoning is critical for machine learning,” says Nailong Zhang, a software engineer at Meta. Meta is using causal inference in a machine-learning model that manages what number of and what sorts of notifications...

Gain a deeper understanding of derivatives with PythonThe derivative approximation implemented in this text is generally known as the “forward difference quotient” and is one in all some ways to perform numerical differentiation. It’s...

We built a neural theorem prover for Lean that learned to unravel a wide range of difficult high-school olympiad problems, including problems from the AMC12 and AIME competitions, in addition to two problems adapted from the IMO.

Hey there Top-gs! Welcome to the primary a part of Statistically Speaking! In this text, we’re gonna dive into some super basic Qs about stats like what the heck it even is and why...

An Interactive Visualisation for You to Experiment WithSo given the LDA boundary, we will make classifications. But how can we draw the boundary line using the LDA algorithm?The road divides the plot where the...

Deriving optimal initial variance of weight matrices in neural network layers with ReLU activation functionInitialization techniques are one in every of the prerequisites for successfully training a deep learning architecture. Traditionally, weight initialization methods...