Practical GenAI (Arabic)

The objective of the sequel is to prepare you to be a professional GenAI engineer/developer. I will take you from the ground-up in the realm of LLMs and GenAI, starting from the very basics to building working and production level apps. The spirit of the sequel is to be “hands-on”. All examples are code-based, with final projects, built step-by-step either in python, Google Colab, and deployed in streamlit. By the end of the courses sequel, you will have built chatgpt clone, Midjourney clone, Chat with your data app, Youtube assistant app, Ask YouTube Video, Study Mate App, Recommender system, Image Description App with GPT-V, Image Generation app with DALL-E and StableDiffusion, Video commentator app using Whisper and others. We will cover prompt engineering approaches, and use them to build custom apps, that go beyond what ChatGPT knows. We will use OpenAI APIs, LangChain and many other tools. We will build together different applications using streamlit as our UI and cloud deployment framework. Streamlit is known of its ease of use, and easy python coding. With the power of GPT models, either by OpenAI, or opensource (like Llama or Mixtral on Huggingface), we will be able to build interesting applications, like chatting with your documents, chatting with youtube videos, building a state-of-the art recommender systems, video auto commentator and translator from one voice to another. We will mix modalities, like Image with GPT-V and DALL-E, Text with ChatGPT: GPT3.5 and GPT4, and voice with Whisper. We will be able to feed the AI models with our custom data, that is not available on the internet, and open models like ChatGPT do not know about. We will cover advanced and state-of-the art topics like RAG models, and LLM Agents. You will also work with different kinds of LLMs, being opensource or not. You will get exposed to GPT models by OpenAI, Llama models by Meta, Gemini and Bard by Google, Orca by Microsoft, Mixtral by Mistral AI and others. You will use pre-trained models, and also finetune them on your own data. We will learn about huggingface and use it for model finetuining using the Parameter Efficient Training or PEFT models. We will use Low-Rank Adaptation or LoRA for efficient training. You will learn how to deploy a model in the cloud, or privately host it for privacy concerns of your company data. You will learn how to use existing pre-trained models as teachers and use model distillation to train your custom version of the model.