How to evaluate large language models using Prompt Engineering | Testing and Improving with PyTorch
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Sobre esta lección
#FreeBirdsCrew #PromptEngineering #Prompt #LargeLanguageModels #ArtificialIntelligence #DeepLearning In this second video of the Prompt Engineering course, we'll dive into evaluating and testing prompt engineering models. You'll learn different metrics for evaluating prompt engineering models, techniques for debugging and improving them, and ways to test them on different datasets. We'll also provide practical exercises that will help you evaluate and test prompt engineering models, giving you hands-on experience using different metrics to evaluate your models, debugging and improving them, and testing them on different datasets. 🚀 Learnings: 🤖 Different metrics for evaluating prompt engineering models, including perplexity, accuracy, and human evaluation. 🤖 Techniques for debugging and improving prompt engineering models by analyzing generated responses and identifying common errors or patterns. 🤖 The importance of testing prompt engineering models on different datasets to evaluate their ability to generalize to new and unseen data. 🤖 Tools and techniques, such as visualization tools and cross-validation, that can be used for evaluating and testing prompt engineering models. 🤖 Ongoing evaluation and testing of prompt engineering models to ensure continued performance. 🚀 Follow me on Medium for the latest blogs and projects - https://bit.ly/3JGXqwc 🔖 Machine Learning Roadmap - 🔖 Data Science Roadmap - https://bit.ly/3NtlFjw 🗂️ To get the Source Code, Follow me on GitHub - https://bit.ly/3gg07Uc 🔖 Book your call with me at topmate.io and learn how you can harness the latest technologies power and speed up your learning process. 📲 Book your call at - https://bit.ly/43TLDCD 🤖 Playlists that make you skilled up - 📍 Artificial Intelligence Projects - https://bit.ly/3L8lhEi 📍 Machine Learning - https://bit.ly/3gsuIxb 📍 Face Recognition - https://bit.ly/2YphpHm 📍 Creative Python - https://bit.ly/34nM9wr 📍 Latest Tech Videos - https://bit.ly/2QcaOeW 📱Follow US on Social Media - - Telegram: https://bit.ly/3JJblSC - Youtube: https://bit.ly/38gLfTo - Instagram: http://bit.ly/2N1IMP9 - Twitter: https://bit.ly/40vYMjl ⚡️ Do Like, Comment, Share, and Subscribe to our YouTube Channel for more Videos and Projects. krish naik machine learning, krish naik deep learning, krish naik nlp, large language models krish naik machine learning full course, machine learning tutorial, machine learning interview questions, machine learning projects in python, prompt engineering, large language models explained, large language models from scratch, large language models stanford, large language models architecture, large language models playlist, large language model full course, evaluate large language models, test large language models, LLM, prompt engineering course playlist, prompt engineer course, chatgpt, google bard, hugging face transformer, meta llama, claude2, anthropic ai, chatbot, how to build chatbot in python, Google, Microsoft, Amazon, Telsa, Twitter, JP Morgan, Salesforce, AI
Sobre este curso
Welcome to "Mastering Prompt Engineering: A Comprehensive Guide," a playlist dedicated to teaching you everything you need to know about prompt engineering, a cutting-edge field within natural language processing (NLP) that focuses on building models that can generate high-quality text outputs in response to prompts or input. This playlist consists of six in-depth videos that will take you from the basics of prompt analysis to advanced techniques for fine-tuning pre-trained language models. Here's what you can expect to learn: 1. Introduction to Prompt Engineering: In this video, we will introduce you to prompt engineering and explain why it is an important field for NLP. We will cover the basics of prompt analysis, including how to deconstruct prompts and identify key features and constraints. 2. Building Your First Prompt Engineering Model: In this video, we will walk you through the process of building your first prompt engineering model. We will cover the key steps, from selecting a pre-trained language model to fine-tuning it for a specific task and input. 3. Evaluating Prompt Engineering Models: In this video, we will discuss the importance of evaluating prompt engineering models and how to do it effectively. We will cover key metrics and techniques for evaluating the quality and performance of your models. 4. Advanced Techniques for Prompt Engineering: In this video, we will dive deeper into advanced techniques for prompt engineering. We will cover topics such as transfer learning, meta-learning, and zero-shot learning, and how to apply these techniques to improve the performance and flexibility of your models. 5. Applications of Prompt Engineering: In this video, we will explore the wide range of applications for prompt engineering, from chatbots and virtual assistants to language translation and content generation. We will discuss the benefits and limitations of prompt engineering for each application, and provide real-world examples. 6. Best Practices for Prompt Engineering: In this final video, we will share our best practices for prompt engineering, including tips and tricks for building high-quality models that are accurate, coherent, and contextually appropriate. We will also discuss common pitfalls and how to avoid them. By the end of this playlist, you will have a deep understanding of prompt engineering and how to apply it to real-world problems. So, whether you are a beginner or an experienced practitioner, join us on this journey and master the art of text generation with prompt engineering.
Lo que aprenderás en este curso:
- Comprender los fundamentos y conceptos clave de Prompt Engineering Full Course with LLM
- Aplicar técnicas y métodos prácticos de Prompt Engineering Full Course with LLM
- Desarrollar habilidades profesionales en Prompt Engineering Full Course with LLM
- Resolver problemas reales relacionados con Prompt Engineering Full Course with LLM