A Deep Dive into Federated Learning of LLMs
Federated Learning (FL) enables privacy-preserving training of Large Language Models (LLMs) across decentralized data sources,
ADaSci Expert
Sachin Tripathi
Sachin Tripathi is the Manager of AI Research at AIM, with over a decade of experience in AI and Machine Learning. An expert in generative AI and large language models (LLMs), Sachin excels in education, delivering effective training programs. His expertise also includes programming, big data analytics, and cybersecurity. Known for simplifying complex concepts, Sachin is a leading figure in AI education and professional development.
DeepSeek-Prover-V2 for Mastering Mathematical Reasoning
DeepSeek-Prover-V2 combines informal reasoning and formal proof steps to solve complex theorems , achieving top
A Practical Guide to Enabling AI Agent Browser Control using Browser-use
Browser-Use is an open-source Python library that lets LLM-powered agents interact with websites via natural
Chartered Data Scientist (CDS™)
The highest distinction in the data science profession. Not just earn a charter, but use it as a designation.
The Rise of Multilingual LLMs: Cohere Unveils Aya 23
Cohere unveils Aya 23, advanced multilingual models, trained on 23 languages, enhancing global AI communication.
A Practical Guide to Text Generation from Complex PDFs using RAG with LlamaParse
Understand and implement advanced RAG on complex PDFs with LlamaParse.
A Practical Guide to Tracing and Evaluating LLMs Using LangSmith
Explore LangSmith, a platform for enhancing LLM transparency and performance through comprehensive tracing and evaluation
Analysing Generative AI Embeddings with Azure AI Hub: A Hands-on Guide
Explore and implement various embedding models in Azure AI Hub to enhance contextual search and
Building Knowledge Graphs to Enhance Data-Driven Decision-Making
Knowledge graphs, built using graph databases, capture data relationships for efficient modelling and reasoning. This
How to Enhance RAG Models with Pinecone Vector Database?
Discover the transformative power of Retrieval Augmented Generation (RAG) models in unleashing the full potential
