Gaurav Gada

Applied Scientist, (Part-Time) Musician, Electrical Engineer, Runner

The Amazon Spheres, Seattle, WA

Hello! I'm an Applied Scientist with over 10 years of industry experience focused on NLP with a recent focus on Generative AI and agentic automation. In the past I've worked on content moderation, AI safety, conversational agents, shift scheduling and labor cost optimization. I love to combine my creative and scientific approach to solve complex problems in an applied and practically useful way and intend to keep pushing the boundaries of what's possible with AI abd its applications. Thanks for dropping by—I’m glad you’re here! Let’s explore what’s next, together. Do drop a note--apart from deep learning (pun intended), I'm always down for deep conversations with practitioners in the field.

Posts

When Should You Build an AI Agent? A Practical Decision Framework

Practical framework to determine when AI agents make sense for your use case. Learn when to build agents and when simpler approaches like prompt engineering or RAG work better.

Mistral 7B on consumer hardware

Run Mistral 7B locally on Mac with Ollama for fast seed data generation. Learn CLI setup, prompt formatting, and downstream parsing to generate thousands of samples on consumer hardware.

Finding the right words

Understand how LLMs choose words during generation. Learn temperature, top-k, and top-p sampling strategies to balance coherence, diversity, and task-appropriateness in generated text.

Paper Review - Embers of Autoregression

Critical review of LLM limitations in low-probability situations. Explores why AI practitioners should understand autoregressive training pressures before deploying LLMs for tasks requiring precise reasoning or uncommon patterns.

Multi-label text classification

Learn to build a multi-label text classifier using DistilBERT with imbalanced classes. Covers binary cross-entropy loss, multi-hot encoding, and practical implementation strategies for handling multiple labels.

Library version mismatches declared not safe

Critical lessons on matching Python package versions between model development and inference. Learn about safetensors format advantages and why version mismatches cause production failures.

Mining word collocations

Extract common bigrams and trigrams from text using Gensim and NPMI scoring. Learn to mine jargon, phrases, and collocations from customer reviews, feedback, and text corpora.

Science Talk: Generative LLMs

Comprehensive introduction to generative LLMs covering basics, training processes, and real-world applications. Slides from talk delivered to 70+ attendees.

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