Advaced Practical GenAI - English version

About this course

Disclaimer: The audio narration in this course is AI-generated, based on human-written scripts and human-designed slides. The use of AI narration is to improve clarity for learners, while all instructional content remains instructor-created.

This is Part 3 of the Practical GenAI Sequel.

The goal of this sequel is to prepare you to become a professional GenAI engineer or developer. We’ll start from the foundations of LLMs and GenAI and progress to building fully working, production-ready applications.

The sequel follows a hands-on approach. Every concept is taught through code-based examples, with final projects built step by step in Python, Google Colab, and deployed using Streamlit.

By the end of the full sequel, you will have built a diverse range of applications, including a ChatGPT clone, MidJourney-style image generator, Chat with Your Data app, YouTube Assistant, Ask YouTube Video app, Study Mate, Recommender system, Image Description app with GPT-V, Image Generation apps with DALL·E and Stable Diffusion, Video Commentator with Whisper, and more.

In this part, you will work with different kinds of LLMs—both open-source and proprietary. You’ll get hands-on exposure to:

• GPT models by OpenAILLaMA models by MetaGemini and Bard by GoogleOrca by Microsoft
• Mixtral by Mistral AI…and other emerging models.
• You’ll learn how to use pre-trained models, and also how to fine-tune them on your own data.

We’ll dive into Hugging Face for model fine-tuning, leveraging Parameter-Efficient Fine-Tuning (PEFT) methods. You’ll work with Low-Rank Adaptation (LoRA) to train models efficiently.

You

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