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

Build agents that navigate and interact with websites, and learn how to make them more reliable.

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

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

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

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

Understand how LLMs predict the next token and how techniques like KV caching can speed up text generation. Write code to serve LLM applications efficiently to multiple users.

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

Learn advanced retrieval techniques to improve the relevancy of retrieved results. Learn to recognize poor query results and use LLMs to improve queries.

Get an introduction to tuning and evaluating LLMs using Reinforcement Learning from Human Feedback (RLHF) and fine-tune the Llama 2 model.

Discover when to use finetuning vs prompting for LLMs. Select suitable open-source models, prepare data, and train & evaluate for your specific domain.

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