Samadrita Ghosh

Natural Language Understanding powers assistants and support systems – and deep reinforcement learning (DRL) is emerging as a feedback-driven way to improve them. This article explains what DRL is and where it fits in today’s NLU stack. You’ll learn how DRL refines dialogue/summarization, how reward models align behavior, and how to run a hands-on demo that simulates RLHF feedback signals. https://lnkd.in/gZpRBwQU

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Meet Anand Trivedi, author of "Building LLMs with PyTorch" — your essential guide for software engineers and AI enthusiasts ready to build cutting-edge large language models. 🤖 With 15 years of industry experience and a passion for Artificial Intelligence and Machine Learning, Anand Trivedi brings a wealth of practical knowledge to this book. As a founder and key contributor to multiple startups, he has a proven track record of taking AI technologies from concept to scale. Recognized as one of the "40 under 40 AI engineers" in 2024, his expertise is both deep and current, making him a trusted authority in the field. This book provides a strategic and hands-on framework for mastering PyTorch and creating your own LLMs from the ground up. You will learn to: ➡️ Master PyTorch fundamentals, from tensors and autograd to model building. ➡️ Build sophisticated computer vision models for object detection and segmentation. ➡️ Develop RNNs and LSTMs for natural language processing and text generation. ➡️ Implement the powerful Transformer architecture and build your very own mini-GPT. ➡️ Create generative models like VAEs and GANs for a wide range of applications. By the end, you will be equipped to build, train, and deploy sophisticated LLMs, ensuring you not only adapt to the AI revolution but truly thrive within it. This is a must-read for software engineers, AI researchers, and professionals who want to lead their careers into the future of AI. 📘 Grab your copy today! ✨ Worldwide: https://lnkd.in/g4jtJk-a India: https://lnkd.in/gxbr6JSc Team Manish Jain Varun Jain Nrip Jain Mukesh Jha Sanjay Jain #PyTorch #LLM #LargeLanguageModels #DeepLearning #MachineLearning #AI #ArtificialIntelligence #Python #AnandTrivedi #NewRelease #TechBooks

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Automate detection of unreliable LLM outputs by combining MLflow tracing with Cleanlab’s Trustworthy Language Models (TLM). 🚀 This blog post covers: ✅ Setting up MLflow to capture complete LLM interactions, including system prompts. ✅ Retrieving traces and efficiently batch-evaluating them with TLM to get trustworthiness scores and explanations—without ground truth labels. ✅ Tagging each MLflow trace with its trust score and explanation for ongoing review. ✅ Using MLflow Evaluation with a custom TLM metric to systematically compare model outputs. For the full guide and code samples, read more ➡️ https://lnkd.in/eHNWQrrK cc Chris Mauck, Data Scientist at Cleanlab #opensource #llm #oss #mlflow #cleanlab

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One of the biggest challenges in radiological AI research is the lack of large datasets to train AI models, Akshay Chaudhari explains. "If we have small data sets, how can we still build high-quality AI models?" he asks. The RoentGen open AI model, developed by AIMI Center Associate Director Dr. Akshay Chaudhari, AIMI Center Director Curtis Langlotz, AIMI faculty Sergios Gatidis and colleagues, seeks to address this by producing realistic synthetic X-rays from medical descriptions. "The purpose of RoentGen would be to produce additional data that would be used to train an AI software tool to provide additional accuracy for radiologists who are working in the clinic, helping them identify disease sooner and identify disease that they might miss, and then extend it to other areas like CTs and MRIs and ultrasounds," Dr. Langlotz says. The model could also help fill other critical gaps. "RoentGen could be used to reduce bias, and we implement algorithms by creating synthetic data for some subgroups where we don't have enough training data," Dr. Langlotz continues. "That means better privacy for patients. It means more accurate AI models and it means more responsible implementation of AI algorithms." See more in this video from Stanford University School of Medicine spotlighting the team's development of RoentGen: https://bit.ly/467QGBi Read the article: https://stan.md/4fT6Tyy #StanfordAIMI #AIMICenter #RoentGen #AIinMedicine

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💡 The Model Context Protocol (MCP) is an open standard and open-source framework developed by Anthropic to standardize how AI systems, especially large language models (LLMs), interact with external tools, systems, and data sources. MCP aims to become a standardized way for AI to interact with your apps on behalf of users. 🤓 Let's dig in to how it works together: https://lnkd.in/eaqMn4Tx

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This webinar with Austin Jang introduces LLM Observability and is intended for engineers building and operating LLM-based systems in production. We’ll demonstrate three common use cases for LLM Observability in Observe: • 🧠 Quality of AI responses – Inspect individual LLM calls within agent traces to understand how prompts, tool selections, and intermediate outputs contributed to incorrect or low-quality responses. • 💰 Token usage – Analyze token consumption patterns to detect cost regressions and inefficiencies. • 🔧 Troubleshooting AI infrastructure – Trace application failures through the full stack, connecting AI app data to infrastructure and APM data. 👉 Register here: https://lnkd.in/gSbGBBvf #GenAI #LLM #Observability

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