Machine Learning Expedition

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Machine Learning Expedition

Active Learning in Machine Learning

Active learning is a machine learning technique that seeks to minimize the amount of labeled data required to train accurate models. In active learning, models are able to interactively query data points from users or oracles. By only requesting labels for the most informative samples, active learning algorithms aim to

A guide to Contrastive Learning

Contrastive learning is an emerging technique in the field of machine learning that is gaining popularity due to its ability to learn powerful representations from unlabeled data. In traditional supervised learning, models are trained on labeled examples, with the labels providing direct supervision for what features the model should learn.

Intro to self-supervised learning

Self-supervised learning is one of the most exciting and rapidly evolving fields in artificial intelligence research today. In self-supervised learning, AI models are able to learn useful representations from unlabeled data by exploiting structure within the data itself. This differs from supervised learning, where models learn from human-labeled data, and

Introduction to Variational Autoencoders

Variational autoencoders (VAEs) are a type of neural network used for unsupervised learning tasks like generative modeling. VAEs provide a probabilistic way to describe an observation in latent space and generate new data samples. In this article, we’ll cover the basics of how variational autoencoders work and some of

What is a Generative Adversarial Network?

Generative Adversarial Networks (GANs) are a fascinating and innovative class of artificial intelligence algorithms. They are part of the larger family of neural networks and have been especially impactful in the field of computer vision, though their applications are vast and varied. This blog post aims to demystify GANs and

How to run some open-source large language models?

How to run open-source large language models such as Falcon-7B, Llama-2 etc

Deploying Machine Learning Models at scale

Deploying machine learning models in production is challenging. It requires planning for scale, automating workflows, managing versions, monitoring performance, and maintaining models over time. This guide covers everything you need to successfully deploy ML models at scale. Before we dive deep into the different concepts about machine learning model deployment.