Banish Deep Ojha

Improve LLM reasoning with reinforcement fine-tuning and reward functions.

Build agents that write and execute code to perform complex tasks, using Hugging Face’s smolagents.

Understand and implement the attention mechanism, a key element of transformer-based LLMs, using PyTorch.

Understand the transformer architecture that powers LLMs to use them more effectively.

Learn how to use and prompt OpenAI's o1 model for complex reasoning tasks.

Build agents that collaborate to solve complex business tasks.

Try out the features of the new Llama 3.2 models to build AI applications with multimodality.

Build an interactive system for querying video content using multimodal AI

Learn best practices for multimodal prompting using Google’s Gemini model.

Systematically improve the accuracy of LLM applications with evaluation, prompting, and memory tuning.

Learn how to securely fine-tune large language models (LLMs) with private data using federated methods, enhancing data privacy, minimizing risks of data leakage, and optimizing efficiency through Parameter-Efficient Fine-Tuning (PEFT) and Differential Privacy.

Learn the essential steps to pretrain a large language model from scratch.

Train your machine learning models using cleaner energy sources.

Deploy AI for edge devices and smartphones. Learn model conversion, quantization, and how to modify for deployment on diverse devices.

Automate business workflows with multi-AI agent systems. Exceed the performance of prompting a single LLM by designing and prompting a team of AI agents through natural language.

Build autonomous agents that intelligently navigate and analyze your data. Learn to develop agentic RAG systems using LlamaIndex, enabling powerful document Q&A and summarization. Gain valuable skills in guiding agent reasoning and debugging.

Learn prompt engineering for vision models using Stable Diffusion, and advanced techniques like object detection and in-painting.

Learn how to quantize any open-source model. Learn to compress models with the Hugging Face Transformers library and the Quanto library.

Learn how to build and use knowledge graph systems to improve your retrieval augmented generation applications. Use Neo4j's query language Cypher to manage and retrieve data.

Learn best practices for prompting and selecting among Meta Llama 2 & 3 models. Interact with Meta Llama 2 Chat, Code Llama, and Llama Guard models.

Learn how to deploy an LLM-based application into production using serverless technology. Learn to prompt and customize LLM responses with Amazon Bedrock.

Learn to build six applications powered by vector databases, including semantic search, retrieval augmented generation (RAG), and anomaly detection.

Learn LLMOps best practices as you design and automate steps to fine-tune and deploy an LLM for a specific task.

Learn MLOps tools for managing, versioning, debugging, and experimenting in your ML workflow.

Create and demo machine learning applications quickly. Share your app with teammates and beta testers on Hugging Face Spaces.

Learn and build diffusion models from the ground up, understanding each step. Learn about diffusion models in use today and implement algorithms to speed up sampling.