Build a transformer model with natural language processing to create new cocktail recipes from a cocktail database.

An easy explanation of how self-attention works and a brief look at the evolution of large language models.

Wish to generate high quality, realistic, controllable music using AI? Meta's new MusicGen is the answer.

Explore the intriguing journey from Recurrent Neural Networks (RNNs) to Transformers in the world of Natural Language Processing in our latest piece: 'The Trans

How Transformers addressed the challenges posed by RNNs.

Zero-shot text classification using trnasformers and TARSclassifier.

You want an intro to embeddings, learn about the cool things done at Yelp, and later play with the off-the-shelf models available on Hugging Face? Let's dive in

Text classification case study with a product-driven twist. We're building various models (logreg, RNN, transformers) and compare their quality and performance

Explore Mamba, an innovative architecture surpassing Transformers in efficiency for long sequences, promising advancements in AI with its flexible design.

CPU & GPU - The Basics - A digestible high-level overview of what happens in The Die

Explore a cutting-edge pipeline for detecting and explaining anomalous reviews in online platforms like Amazon.

Explore findings from a study on an anomaly detection pipeline for Amazon reviews using MPNet embeddings.

Explore findings from a study on an anomaly detection pipeline for Amazon reviews using MPNet embeddings.

Explore a comprehensive evaluation of anomaly detection methods using Amazon review data.

Explore recent advancements in anomaly detection for texts, focusing on techniques like NLP and machine learning classifiers.

Discover a robust pipeline for detecting and explaining anomalous reviews in online platforms like Amazon.

Speculative decoding is an advanced AI inference technique that is gaining traction in natural language processing (NLP) and other sequence generation tasks.

Transformers are limited by sequence length due to quadratic scaling. Explore solutions like sparse attention, low-rank approximations, and spectral methods.

This report presents a novel approach to cryptocurrency trading using a Transformer-based Deep Reinforcement Learning (DRL) agent.

Model serving has been an active area of research in recent years, with numerous systems proposed to tackle diverse aspects of deep learning model deployment.