Ishmeet
Kaur
Software Engineer
"From Words to Understanding: Demystifying Machine Learning models in Advancing Language Processing"
"From Words to Understanding: Demystifying Machine Learning models in Advancing Language Processing"
10 - 2:30 PM EDT/New York (See in local time)
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04//2024 2:30 PM
04//2024 03:10 PM
America/New_York
#WTGC2024
"From Words to Understanding: Demystifying Machine Learning models in Advancing Language Processing"
#WTGC2024
"From Words to Understanding: Demystifying Machine Learning models in Advancing Language Processing"
https://app.events.ringcentral.com/events/women-in-tech-global-conference-2024/reception
https://www.womentech.net/vfairs
"From Words to Understanding: Demystifying Machine Learning models in Advancing Language Processing"
"From Words to Understanding: Demystifying Machine Learning models in Advancing Language Processing"
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Session: From Words to Understanding: Demystifying Machine Learning models in Advancing Language Processing
In this talk, we will provide an introduction to machine learning and its applications in natural language processing. This hands-on tutorial aims to demystify large language models (LLMs) by providing participants with a comprehensive overview of the key concepts, tools, and techniques needed to understand and work with these models effectively. We will start by defining what machine learning is and provide examples of its use in real-world scenarios. We will then discuss the different types of machine learning and will then dive into the concept of large language models, which are a type of machine learning model that has gained significant attention in recent years.Next, we will provide an overview of the different types of large language models, including GPT-3, BERT, and others, specifically how they work and how they are trained using techniques such as transfer learning and pre-training.
We will also explore the use cases for these models and how they can be utilized to enhance natural language processing tasks such as text generation, translation, and sentiment analysis.
Finally, we will provide a practical guide on how to build and use large language models. This will include an overview of the tools and frameworks available for building machine learning models, as well as tips on how to effectively train and fine-tune models. We will also discuss ethical considerations around the use of large language models and the potential biases that can be introduced.
By the end of this talk, you will have a solid understanding of the fundamentals of machine learning and large language models, and be equipped with the knowledge and resources needed to start building and using these powerful tools in your own work.
This tutorial is suitable for data scientists, machine learning practitioners, software developers, and anyone who is interested in understanding and working with LLMs.
Key Takeaways
- By the end of the tutorial, participants will be able to code up and experience the impact of Machine learning models , specifically LLMs without any prior knowledge.
- The participants would have a solid understanding of LLMs and their capabilities, limitations, and potential applications.
- The participants would also have acquired the skills and knowledge needed to build and deploy LLM-based models and applications in their own projects.
- This tutorial is suitable for data scientists, machine learning practitioners, software developers, and anyone who is interested in understanding and working with LLMs.
- This tutorial will make the participant see clearly the link between code and theory for ML models and algorithms.
Bio
I am Ishmeet Kaur, a software engineer. I reside in San Diego, CA. I obtained a bachelor's degree from Purdue University, where I majored in Computer Engineering and minored in Management.I am self-taught machine learning practitioner who focuses on natural language processing with Large Language Models (LLMs). My key experiences include the design of data ingestion pipelines for large-scale training of LLMs and the use of LLMs to filter through cutting-edge medical research publications. I have also been appointed as a Machine Learning Expert Analyst for my contributions to open-source projects such as AI-vs-COVID.
Outside of work, I love traveling, painting, and hiking. I am also a big coffee and tea enthusiast.
I am currently focused on being an expert in software engineering for machine learning and getting involved in as I many projects as possible to constantly learn and grow.
Outside of work, I love traveling, painting, and hiking. I am also a big coffee and tea enthusiast.
