Cameron Wolfe, PhD

Cameron Wolfe, PhD

Director of AI | Rebuy Engine

Cameron earned his Ph.D. in Computer Science from Rice University (advised by Dr. Anastasios Kyrillidis) in Houston, TX. His research interests are related to math and machine/deep learning, including non-convex optimization, theoretically-grounded algorithms for deep learning, continual learning, and practical tricks for building better systems with neural networks. Cameron is currently the Director of AI at Rebuy, a personalized search and recommendations platform for D2C e-commerce brands. He works with an amazing team of engineers and researchers to investigate topics such as language model agent systems, personalized product ranking, search relevance, and more.

All Sessions by Cameron Wolfe, PhD

Prompt Engineering: From Few Shot to Chain of Thought

LLMs | Intermediate

The popularization of large language models (LLMs) has completely shifted how we solve problems as humans. In prior years, solving any task (e.g., reformatting a document or classifying a sentence) with a computer would require a program (i.e., a set of commands precisely written according to some programming language) to be created. With LLMs, solving such problems requires no more than a textual prompt. In this session, we will provide a basic primer on the topic of prompt engineering, as well as cover examples of notable prompt engineering techniques ranging from basic strategies like few-shot learning to more advanced approaches like chain of thought prompting.

Prompt Engineering: From Few Shot to Chain of Thought

LLMs | Intermediate

The popularization of large language models (LLMs) has completely shifted how we solve problems as humans. In prior years, solving any task (e.g., reformatting a document or classifying a sentence) with a computer would require a program (i.e., a set of commands precisely written according to some programming language) to be created. With LLMs, solving such problems requires no more than a textual prompt. In this session, we will provide a basic primer on the topic of prompt engineering, as well as cover examples of notable prompt engineering techniques ranging from basic strategies like few-shot learning to more advanced approaches like chain of thought prompting.

Prompt Engineering: From Few Shot to Chain of Thought

LLMs | Intermediate

The popularization of large language models (LLMs) has completely shifted how we solve problems as humans. In prior years, solving any task (e.g., reformatting a document or classifying a sentence) with a computer would require a program (i.e., a set of commands precisely written according to some programming language) to be created. With LLMs, solving such problems requires no more than a textual prompt. In this session, we will provide a basic primer on the topic of prompt engineering, as well as cover examples of notable prompt engineering techniques ranging from basic strategies like few-shot learning to more advanced approaches like chain of thought prompting.

Prompt Engineering: From Few Shot to Chain of Thought

LLMs | Intermediate

The popularization of large language models (LLMs) has completely shifted how we solve problems as humans. In prior years, solving any task (e.g., reformatting a document or classifying a sentence) with a computer would require a program (i.e., a set of commands precisely written according to some programming language) to be created. With LLMs, solving such problems requires no more than a textual prompt. In this session, we will provide a basic primer on the topic of prompt engineering, as well as cover examples of notable prompt engineering techniques ranging from basic strategies like few-shot learning to more advanced approaches like chain of thought prompting.

Open Data Science

 

 

 

Open Data Science
One Broadway
Cambridge, MA 02142
info@odsc.com

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