Understanding

Introduction to AutoencodersPhoto: Michela Massi via Wikimedia Commons,(https://commons.wikimedia.org/wiki/File:Autoencoder_schema.png)Autoencoders are a category of neural networks that aim to learn efficient representations of input data by encoding after which reconstructing it. They comprise two foremost parts:...

In the trendy, fast-paced era, where the world is dependent upon AI-driven decisions, trust is paramount. Character.AI, a rising star in conversational AI, tackles this very concern. It goals to remodel digital interactions into...

A synthetic intelligence (AI) model that focuses on reading and understanding human emotions has emerged. The intention is to read 53 sorts of emotions, accurately discover human intentions, and help the large-scale language...

I’ve recently been tinkering with deep learning models in Tensorflow, and have accordingly been introduced to managing data as tensors.As a Data Engineer that works all day in tables that I can easily slice,...

LDA Convergence Explained with a Dog Pedigree Model“What if my a priori understanding of dog breed group distribution is inaccurate? Is my LDA model doomed?”My wife asked.Welcome back to part 2 of the series,...

Math behind this parameter efficient finetuning methodNice-tuning large pre-trained models is computationally difficult, often involving adjustment of thousands and thousands of parameters. This traditional fine-tuning approach, while effective, demands substantial computational resources and time,...

An in-depth exploration of histograms and KDEA histogram is a graph that visualizes the frequency of numerical data. It is usually utilized in data science and statistics to have a raw estimate of the...

16 min read·15 hours agoThis tutorial explores how covariates influence A/B testing precision in a randomized experiment. A properly randomized A/B test calculates the lift by comparing the common end result within the treatment...