Machine Learning

GPT-4 won’t be your lawyer anytime soon — GPT-4 was announced by OpenAI in March with impressive demonstrations and outstanding claims. Most of these claims come from their own evaluation of GPT-4. OpenAI used many existing professional and academic exams for this evaluation. But evaluating large language models on public benchmarks is extremely challenging. Models such as GPT-4…

Gain insight into the fundamental processes involved in building a decision tree classifier from the ground — Decision trees serve various purposes in machine learning, including classification, regression, feature selection, anomaly detection, and reinforcement learning. They operate using straightforward if-else statements until the tree’s depth is reached. Grasping certain key concepts is crucial to fully comprehend the inner workings of a decision tree.

Understanding the most underrated trick in applied Machine Learning — Hashing is one of the most common “tricks” used in industrial Machine Learning applications, yet it doesn’t get nearly as much attention as it deserves. The biggest advantage of hashing, especially in modern recommender systems, is its finite-memory guarantee: without hashing, it would be highly impractical to learn the relevance…

An introduction to PyTorch’s training loop and general approach to tackle the library’s steeper initial learning curve — In this post we will cover how to implement a logistic regression model using PyTorch in Python. PyTorch is one of the most famous and used deep learning frameworks by the community of data scientists and machine learning engineers in the world, and thus learning this tool becomes an essential…