Overview

This course is diving into Generative AI State-Of-Art Scientific Challenges. It helps to uncover ongoing problems and develop or customize your Own Large Models Applications. Course mainly is suitable for any candidates(students, engineers,experts) that have great motivation to Large Language Models with Todays-Ongoing Challenges as well as their deeployment with Python Based and Javascript Web Applications, as well as with C/C++ Programming Languages. Candidates will have deep knowledge on TensorFlow , Pytorch, Keras models, HuggingFace with Docker Service. In addition, one will be able to optimize and quantize TensorRT frameworks for deployment in variety of sectors. Moreover, They will learn deployment of LLM quantized model to Web Pages developed with React, Javascript and FLASKHere you will also learn how to integrate Reinforcement Learning(PPO) to Large Language Model, in order to fine them with Human Feedback based. Candidates will learn how to code and debug in C/C++ Programming languages at least in intermediate level.LLM Models used: The Falcon, LLAMA2, BLOOM, MPT, Vicuna,FLAN-T5, GPT2/GPT3, GPT NEOXBERT 101, Distil BERTFINE-Tuning Small Models under supervision of BIG ModelsImage Generation:LLAMA modelsGemini Dall-E OpenAIHugging face ModelsLearning and Installation of Docker from scratchKnowledge of Javscript, HTML ,CSS, BootstrapReact Hook, DOM and Javacscript Web DevelopmentDeep Dive on Deep Learning Transformer based Natural Language ProcessingPython FLASK Rest API along with MySqlPreparation of DockerFiles, Docker Compose as well as Docker Compose Debug fileConfiguration and Installation of Plugin packages in Visual Studio CodeLearning, Installation and Confguration of frameworks such as Tensorflow, Pytorch, Kears with docker images from scratchPreprocessing and Preparation of Deep learning datasets for training and testingOpenCV DNN with C++ InferenceTraining, Testing and Validation of Deep Learning frameworksConversion of prebuilt models to Onnx and Onnx Inference on images with C++ ProgrammingConversion of onnx model to TensorRT engine with C++ RunTime and Compile Time APITensorRT engine Inference on images and videosComparison of achieved metrices and result between TensorRT and Onnx InferencePrepare Yourself for C++ Object Oriented Programming Inference!Ready to solve any programming challenge with C/C++ Read to tackle Deployment issues on Edge Devices as well as Cloud AreasLarge Language Models Fine TunningLarge Language Models Hands-On-Practice: BLOOM, GPT3-GPT3.5, FLAN-T5 familyLarge Language Models Training, Evaluation and User-Defined Prompt IN-Context Learning/On-Line LearningHuman FeedBack Alignment on LLM with Reinforcement Learning (PPO) with Large Language Model: BERT and FLAN-T5How to Avoid Catastropich Forgetting Program on Large Multi-Task LLM Models.How to prepare LLM for Multi-Task Problems such as Code Generation, Summarization, Content Analizer, Image Generation.Quantization of Large Language Models with various existing state-of-art techniquesImportante Note: In this course, there is not nothing to copy & paste, you will put your hands in every line of project to be successfully LLM and Web Application Developer!You DO NOT need any Special Hardware component. You will be delivering project either on CLOUD or on Your Local Computer.

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