In-context Learning Courses and Certifications

Stanford Seminar 2022 - Transformer Circuits, Induction Heads, In-Context Learning

Explore reverse engineering of transformer language models, focusing on induction head circuits and their role in in-context learning. Gain insights into neural network interpretability and AI safety.

• YouTube
• 1 hour
• On-Demand
• Free Video

Advanced Prompt Engineering for Everyone

90 ratings at Coursera

Unlock AI's potential through advanced prompt engineering techniques. Master in-context learning, RAG, template-based outputs, and real-world applications for enhanced productivity and creativity.

• Coursera
• 8 hours 49 minutes
• On-Demand
• Free Online Course (Audit)

Generative AI and Model Selection

Explore generative AI model selection, deployment options, and performance optimization. Master evaluation techniques and troubleshooting strategies for effective AI implementation in real-world projects.

• Coursera
• 3 hours 12 minutes
• On-Demand
• Free Online Course (Audit)

Fundamentals of AI Agents Using RAG and LangChain

Explore RAG, prompt engineering, and LangChain to build AI agents. Gain hands-on experience with LLMs, develop applications, and complete a real-world project to boost your AI career.

• Coursera
• 6 hours 59 minutes
• On-Demand
• Free Online Course (Audit)

Association for Computing Machinery (ACM)

In-Context Learning: How to Stop Worrying and Love Applied Information Retrieval - Lecture 1

Explore in-context learning in LLMs and its impact on applied information retrieval, focusing on practical applications and overcoming challenges.

• YouTube
• 13 minutes
• On-Demand
• Free Video

MLOps World: Machine Learning in Production

How Many Labelled Examples Do You Need for a BERT-sized Model to Beat GPT4 on Predictive Tasks?

Explore the effectiveness of in-context learning vs. labelled data for predictive tasks, comparing BERT-sized models to GPT-4 in text categorization and entity recognition.

• YouTube
• 28 minutes
• On-Demand
• Free Video

Simons Institute

Towards Understanding Modern Alchemy - Transformers as a Computational Model

Explore in-context learning of formal languages, comparing Transformers to other models and examining specialized "n-gram heads" for improved performance in language modeling.

• YouTube
• 37 minutes
• On-Demand
• Free Video

Association for Computing Machinery (ACM)

Adaptive In-Context Learning with Large Language Models for Bundle Generation - Recommendation Systems

Explore adaptive in-context learning techniques for bundle generation using large language models in recommendation systems.

• YouTube
• 15 minutes
• On-Demand
• Free Video

Institute for Pure & Applied Mathematics (IPAM)

The Dynamics of Memorization and Generalization in Deep Learning

Explore the complex interplay between memorization and generalization in deep learning models, examining strategies to mitigate unwanted memorization and the impact of pruning techniques on learning outcomes.

• YouTube
• 52 minutes
• On-Demand
• Free Video

Institute for Pure & Applied Mathematics (IPAM)

Two Stories in Mechanistic Interpretation of Natural and Artificial Neural Computation

Explore two stories in mechanistic interpretation of neural computation, covering in-context learning in Transformers and insights into linear regression tasks using linear attention models.

• YouTube
• 55 minutes
• On-Demand
• Free Video

Simons Institute

Toward Understanding In-Context Learning

Dive into the mechanisms of in-context learning in large language models, exploring theoretical explanations and simplified data distributions with Stanford's Tengyu Ma.

• YouTube
• 1 hour 30 minutes
• On-Demand
• Free Video

Simons Institute

In-Context Principle Learning from Mistakes

Explore Uri Alon's insights on in-context principle learning from mistakes, focusing on emerging generalization settings in AI and machine learning.

• YouTube
• 58 minutes
• On-Demand
• Free Video

Graham Neubig

CMU Advanced NLP: How to Use Pre-Trained Models

Explore advanced techniques for leveraging pre-trained models in NLP, including fine-tuning, linear probing, and in-context learning. Gain insights into empirical observations and mental models for effective implementation.

• YouTube
• 1 hour 17 minutes
• On-Demand
• Free Video

Data Science Dojo

AI Mastery: Ultimate Crash Course in Prompt Engineering for Large Language Models

Master prompt engineering for AI models: craft effective prompts, explore techniques like n-shot and chain-of-thought, and learn practical tips to enhance communication with language models.

• YouTube
• 1 hour 6 minutes
• On-Demand
• Free Video

Simons Institute

Pretraining Task Diversity and the Emergence of Non-Bayesian In-Context Learning for Regression

Explores how task diversity in pretraining affects transformers' in-context learning abilities for regression, revealing a threshold beyond which models can solve novel tasks and outperform Bayesian estimators.

• YouTube
• 1 hour 6 minutes
• On-Demand
• Free Video