In partnership with AMD, this course teaches how to apply fine-tuning and reinforcement learning to improve LLM behavior, reasoning, and safety. You will learn about the post-training lifecycle, core techniques like RLHF and LoRA, and how to design evaluations to detect issues like reward hacking and diagnose failures.
This professional certificate teaches how to build and train deep learning models using PyTorch. It covers applying transfer learning and fine-tuning to pretrained models for computer vision and natural language processing.
A specialization that teaches how to deploy machine learning models on devices, train and run models in browsers and mobile applications, and retrain deployed models while protecting privacy.
Taught by instructors from LiveKit and an Andreessen Horowitz portfolio company, this course covers how to build scalable voice agents using a cloud infrastructure. It delves into the components of a voice pipeline and real-time networking protocols.
This course teaches how to build multimodal search and RAG systems. It covers implementing contrastive learning for modality-independent embeddings, building multimodal RAG systems that reason over multimodal context, and implementing industry applications like multi-vector recommender systems.
This intermediate course teaches best practices for using Claude Code to improve your coding workflow. You will learn to explore, develop, test, refactor, and debug codebases with this highly agentic AI assistant.
This course introduces Machine Learning Operations tools to manage the complexities of AI projects. You will learn to use Weights & Biases to track experiments, version data, and collaborate. The course covers instrumenting a Jupyter notebook, managing hyperparameters, logging metrics, and tracing prompts and responses to LLMs over time.
This course, offered by DeepLearning.AI and taught by the founder and CEO of crewAI, focuses on building multi-agent systems to automate complex business processes using the open-source crewAI library.
This course teaches you how to use open-source models from the Hugging Face Hub for various tasks like NLP, audio, and image processing. You will learn to use the transformers library to perform these tasks with just a few lines of code and deploy your applications using Gradio and Hugging Face Spaces.
This course, developed in collaboration with Hugging Face, teaches the fundamentals of model quantization. You will learn to compress large models, making them more accessible and efficient, using the Hugging Face Transformers library and Quanto.
Learn how to make safer LLM apps by attacking various chatbot applications using prompt injections to understand security failures. This course, in collaboration with Giskard, teaches industry-proven red teaming techniques to proactively test, attack, and improve the robustness of your LLM applications.
Taught by the co-founder & CEO of GuardrailsAI, this course teaches you to use guardrails to prevent common LLM issues like hallucinations and sensitive information leaks. You'll add guardrails to a RAG-powered chatbot and learn to build custom protections.
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