All courses
Mustafa KapadiaMaven
This course is designed to help Product Managers automate 30+ key PM tasks using AI within the first hour. You will learn to craft precise prompts, build the right mindset, and take automation to the next level by building agents (GPTs) without coding. The course includes interactive live sessions, lifetime access to materials, and a project to apply your learnings.
Harrison ChaseDeepLearning.AI
In this course, you will learn how to build an agent with long-term memory by creating a personal email agent that can respond, ignore, and notify the user using writing, scheduling, and memory tools. You’ll develop your agent’s memory by adding facts to its memory store, providing examples to learn the user’s preferences, and optimizing system prompts to evolve instructions based on previous responses. This course will equip you with the skills to integrate semantic, episodic, and procedural memory into AI agents, enhancing their functionality and user interaction.
Mark LaurenceMaven
Participants will learn the five strategic steps to build an AI-forward business, master AI prompting, and deploy a framework-led diagnosis of their company's AI maturity. The course aims to transform senior leadership teams from AI novices to AI-literate leaders capable of driving AI adoption at scale.
Nicolas BoucherMaven
In this course, you will learn how to give advanced instructions to Generative AI, use it for financial analysis, solve confidential data issues, and build your own GPT for finance. You will also learn to use Python for financial analysis without needing to code. The course includes live sessions, self-paced videos, and a comprehensive set of resources to help you master these skills.
Josh StarmerDeepLearning.AI
Dive deep into the attention mechanism that revolutionized AI with transformers. This course, taught by Josh Starmer, covers the evolution of attention, the role of Query, Key, and Value matrices, and the differences between self-attention and masked self-attention. Gain hands-on experience coding these concepts in PyTorch, and understand how they contribute to building scalable AI applications.
Jay AlammarDeepLearning.AI
In this course, you will learn how a transformer network architecture that powers LLMs works. You'll build the intuition of how LLMs process text and work with code examples that illustrate the key components of the transformer architecture. By the end of this course, you’ll have a deep understanding of how LLMs process language and you’ll be able to read through papers describing models and understand the details that are used to describe these architectures.
Sharon ZhouDeepLearning.AI
Join our new short course, Finetuning Large Language Models! Learn from Sharon Zhou, Co-Founder and CEO of Lamini, and instructor for the GANs Specialization and How Diffusion Models Work. When you complete this course, you will be able to understand when to apply finetuning on LLMs, prepare your data for finetuning, and train and evaluate an LLM on your data. Finetuning allows the model to learn style, form, and can update the model with new knowledge to improve results.
Nikita NamjoshiDeepLearning.AI
In this course, you will gain a conceptual understanding of the RLHF training process, and then practice applying RLHF to tune an LLM. You will explore the two datasets that are used in RLHF training: the 'preference' and 'prompt' datasets. Use the open source Google Cloud Pipeline Components Library to fine-tune the Llama 2 model with RLHF. Assess the tuned LLM against the original base model by comparing loss curves and using the 'Side-by-Side (SxS)' method.
