Khushdeep-singh

Learn linear quantization techniques using the Quanto library and downcasting methods with the Transformers library to compress and optimize generative AI models effectively.

Apply LLMs to your data, build personal assistants, and expand your use of LLMs with agents, chains, and memories.

Master the essential steps of pretraining large language models (LLMs). Learn to create high-quality datasets, configure model architectures, execute training runs, and assess model performance for efficient and effective LLM pretraining.

Learn to build and customize multi-agent systems using the AutoGen. The course teaches you to implement complex AI applications through agent collaboration and advanced design patterns.

Master the art of designing and organizing AI agents. Learn to automate complex, multi-step business processes by creating specialized AI agent teams using the open-source library crewAI.

Dive into the world of text embeddings. This course will guide you through leveraging text embeddings to enhance various natural language processing (NLP) tasks.

Jupyter notebooks for enhancing your skills with ChatGPT based prompt engineering. Harness the potential of large language models and create innovative applications.

Explore Functions, Tools and Agents with LangChain along with LangChain Expression Language

Embark on the "Reinforcement Learning from Human Feedback" course and align Large Language Models (LLMs) with human values.

The course teaches how to fine-tune LLMs using Group Relative Policy Optimization (GRPO)—a reinforcement learning method that improves model reasoning with minimal data. Learn RFT concepts, reward design, LLM-as-a-judge evaluation, and deploy jobs on the Predibase platform.

Master the art of building and enhancing AI agents. Learn to develop flow-based applications, implement agentic search, and incorporate human-in-the-loop systems using LangGraph's powerful components.

Create a continuous integration (CI) workflow for testing LLMs applications in an effective way.

The course equips you with the skills to deploy Large Language Model (LLM)-based applications into production using serverless technology with Amazon Bedrock.

Learn the ins and outs of efficiently serving Large Language Models (LLMs). Dive into optimization techniques, including KV caching and Low Rank Adapters (LoRA), and gain hands-on experience with Predibase’s LoRAX framework inference server.

Dive into advanced quantization techniques. Learn to implement and customize linear quantization functions, measure quantization error, and compress model weights using PyTorch for efficient and accessible AI models.

Enhance the performance and cost-efficiency of large-scale Retrieval Augmented Generation (RAG) applications. Learn to integrate vector search with traditional database operations and apply techniques like prefiltering, postfiltering, projection, and prompt compression.

The course equips developers with techniques to enhance the reliability of LLMs, focusing on evaluation, prompt engineering, and fine-tuning. Learn to systematically improve model accuracy through hands-on projects, including building a text-to-SQL agent and applying advanced fine-tuning methods.

Explore Mistral AI's extensive collection of models. Learn to select, prompt, and integrate Mistral's open-source and commercial models for tasks like classification, coding, and Retrieval Augmented Generation (RAG).

Enhance your skills in prompt engineering for vision models. Learn to effectively prompt, fine-tune, and track experiments for models like SAM, OWL-ViT, and Stable Diffusion 2.0 to achieve precise image generation, segmentation, and object detection.

Understand and build embedding models, focusing on word and sentence embeddings, dual encoder architectures. Learn to train embedding models using contrastive loss, implement them in semantic search and RAG systems.

The course provides a comprehensive guide to optimizing retrieval systems in large-scale RAG applications. It covers tokenization, vector quantization, and search optimization techniques to enhance search quality, reduce memory usage, and balance performance in vector search systems.

"Open Source Models with Hugging Face" course empowers you with the skills to leverage open-source models from the Hugging Face Hub for various tasks in NLP, audio, image, and multimodal domains.

Master the techniques of function-calling and structured data extraction with LLMs. Learn to enhance LLM capabilities, integrate web services, and build practical applications for real-world data usability.

This repository focuses on the cutting-edge features of Llama 3.2, including multimodal capabilities, advanced tokenization, and tool calling for building next-gen AI applications. It highlights Llama's enhanced image reasoning, multilingual support, and the Llama Stack API for seamless customization and orchestration.

Explore LangChain and build powerful chatbots that interact with your own data. Gain insights into document loading, splitting, retrieval, question answering, and more.

Unlock automation and system building with the ChatGPT API. Master chain calls, Python interactions, and create a customer service chatbot in this practical course.

Unlock the potential of finetuning Large Language Models (LLMs). Learn from industry expert, and discover when to apply finetuning, data preparation techniques, and how to effectively train and evaluate LLMs.

Learn to optimize machine learning tasks for environmental sustainability. Discover how to use real-time electricity data and low-carbon energy sources for model training and inference, reducing the carbon footprint of your cloud operations.

This project implements an AI agent that verifies if automated Hercules test runs were executed as intended by comparing planning logs, video evidence, and final outputs. It uses open-source LLMs and computer vision tools to flag deviations, providing detailed reports with technical insights.

Leverage vector databases to swiftly construct a diverse range of applications through "Building Applications with Vector Databases" course!