PyTorch Tutorial- How to Develop Deep Learning Models Part 1: Tensors
PyTorch Tensor Tutorial
Machine Learning
Sequence Classification Using RNNs with FastAI
Implementing an RNN for sequence classification in Python using the FastAI library
Image Classification with PyTorch
Image classification is a common task in computer vision that involves assigning labels or categories to images. It has many real-world applications such as facial recognition, photo organization, medical imaging analysis, self-driving cars, and more. In this post, we will walk through how to build and train an image classifier
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
Tokenization in NLP
Tokenization is one of the most fundamental concepts in natural language processing (NLP). It involves breaking down text into smaller units, or tokens, that are easier for computers to understand. While it may seem simple on the surface, effective tokenization is crucial for building accurate NLP models. In this comprehensive
An Introduction to Linear Regression for Data Science
Linear regression is one of the fundamental machine learning and statistical techniques for modeling the relationship between two or more variables. In this comprehensive guide, we'll cover everything you need to know to get started with linear regression, from basic concepts to examples and applications in Python.
Introduction