Prompt Engineering in Python: GPT, OpenAI, LLMs, HuggingFace
🚀In this tutorial, we’ll dive deep into how LLMs like OpenAI s GPT and Meta’s LLaMA work and how to apply them to real-world NLP tasks. From understanding the basics of tokenization, embeddings, and training to hands-on coding with Prompt Engineering, you’ll gain the skills to tackle text classification, clustering, NER, chatbot building, and more!
We'll be using mainly Python for this tutorial, but the same fundamental concepts about LLMs should apply to any language that you can use to load an LLM easily in.
📚 What You’ll Learn:
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1. The fundamentals of LLMs: embeddings, temperature, and parameters
2. Hands-on integration with OpenAI and HuggingFace models.
3. Prompt engineering techniques, including chain of thought
4. Real-world NLP applications like sentiment analysis and text clustering
5. Building your own chatbot using LLMs
💡 Whether you're an AI enthusiast, data scientist, or developer, this tutorial equips you with cutting-edge skills that employers highly value.
🖥️ Code:
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Link: https://colab.research.google.com/dri...
✨ More courses:
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Check out two free tutorials to further boost your skills:
1️⃣ Create a customer service chatbot for a coffee shop, deploy it in a smartphone app, and integrate NLP techniques: • Build and Deploy an AI Chatbot Using ...
2️⃣ Analyze your favorite TV series with NLP, build character networks, and create a chatbot that mimics your favorite character: • Build an AI/NLP TV Series Analysis Sy...
🔑 TIMESTAMPS
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0:00 - Introduction
1:45- LLMs explained
11:30 - Loading LLMs in code
35:41 - Prompt Engineering
56:06 - NLP with LLMs
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