Learn Everything about Full-Stack Generative AI, LLM MODELS from Udemy

What's inside

Learning objectives

What is docker and how to use docker
Advance docker usage
What are opencl and opengl and when to use ?
(lab) tensorflow and pytorch installation, configuration with docker
(lab)dockerfile, docker compile and docker compose debug file configuration
(lab)different yolo version, comparisons, and when to use which version of yolo according to your problem
(lab)jupyter notebook editor as well as visual studio coding skills
(lab)learn and prepare yourself for full stack and c++ coding exercies
(lab)tensorrt precision float 32/16 model quantiziation
Key differences:explicit vs. implicit batch size
(lab)tensorrt precision int8 model quantiziation
(lab) visual studio code setup and docker debugger with vs and gdb debugger
(lab) what is onnx framework c plus and how to use apply onnx to your custom c ++ problems
(lab) what is tensorrt framework and how to use apply to your custom problems
(lab) custom detection, classification, segmentation problems and inference on images and videos
(lab) basic c ++ object oriented programming
(lab) advance c ++ object oriented programming
(lab) deep learning problem solving skills on edge devices, and cloud computings with c++ programming language
(lab) how to generate high performance inference models on embedded device, in order to get high precision, fps detection as well as less gpu memory consumption
(lab) visual studio code with docker
(lab) gdb debugger with sonarlite and sonarcube debuggers
(lab) yolov4 onnx inference with opencv c++ dnn libraries
(lab) yolov5 onnx inference with opencv c++ dnn libraries
(lab) yolov5 onnx inference with dynamic c++ tensorrt libraries
(lab) c++(11/14/17) compiler programming exercies
Key differences: opencv and cuda/ opencv and tensorrt
(lab) deep dive on react development with axios front end rest api

(lab) deep dive on flask rest api with react with mysql
(lab) deep dive on text summarization inference on web app
(lab) deep dive on bert (llm) fine tunning and emotion analysis on web app
(lab) deep dive on distributed gpu programming with natural language processing (large language models))
(lab) deep dive on generative ai use cases, project lifecycle, and model pre-training
(lab) fine-tuning and evaluating large language models
(lab) reinforcement learning and llm-powered applications, align fine tunning with user feedback
(lab) quantization of large language models with modern nvidia gpu's
(lab) c++ oop tensorrt quantization and fast inference
(lab) deep dive on hugging face library
(lab)translation ● text summarization ● question answering
(lab)sequence-to-sequence models, only encoder based models, only decoder based models
(lab)define the terms generative ai, large language models, prompt, and describe the transformer architecture that powers llms
(lab)discuss computational challenges during model pre-training and determine how to efficiently reduce memory footprint
(lab)describe how fine-tuning with instructions using prompt datasets can improve performance on one or more tasks
(lab)explain how peft decreases computational cost and overcomes catastrophic forgetting
(lab)describe how rlhf uses human feedback to improve the performance and alignment of large language models
(lab)discuss the challenges that llms face with knowledge cut-offs, and explain how information retrieval and augmentation techniques can overcome these challen
Recognize and understand the various strategies and techniques used in fine-tuning language models for specialized applications.
Master the skills necessary to preprocess datasets effectively, ensuring they are in the ideal format for ai training.
Investigate the vast potential of fine-tuned ai models in practical, real-world scenarios across multiple industries.
Acquire knowledge on how to estimate and manage the costs associated with ai model training, making the process efficient and economic
Distributing computing for (ddp) distributed data parallelization and fully shared data parallel across multi gpu/cpu with pytorch together with retrieval augme
The roberta model was proposed in roberta: a robustly optimized bert pretraining approach
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What is docker and how to use docker
Advance docker usage
What are opencl and opengl and when to use ?
(lab) tensorflow and pytorch installation, configuration with docker
(lab)dockerfile, docker compile and docker compose debug file configuration
(lab)different yolo version, comparisons, and when to use which version of yolo according to your problem
(lab)jupyter notebook editor as well as visual studio coding skills
(lab)learn and prepare yourself for full stack and c++ coding exercies
(lab)tensorrt precision float 32/16 model quantiziation
Key differences:explicit vs. implicit batch size
(lab)tensorrt precision int8 model quantiziation
(lab) visual studio code setup and docker debugger with vs and gdb debugger
(lab) what is onnx framework c plus and how to use apply onnx to your custom c ++ problems
(lab) what is tensorrt framework and how to use apply to your custom problems
(lab) custom detection, classification, segmentation problems and inference on images and videos
(lab) basic c ++ object oriented programming
(lab) advance c ++ object oriented programming
(lab) deep learning problem solving skills on edge devices, and cloud computings with c++ programming language
(lab) how to generate high performance inference models on embedded device, in order to get high precision, fps detection as well as less gpu memory consumption
(lab) visual studio code with docker
(lab) gdb debugger with sonarlite and sonarcube debuggers
(lab) yolov4 onnx inference with opencv c++ dnn libraries
(lab) yolov5 onnx inference with opencv c++ dnn libraries
(lab) yolov5 onnx inference with dynamic c++ tensorrt libraries
(lab) c++(11/14/17) compiler programming exercies
Key differences: opencv and cuda/ opencv and tensorrt
(lab) deep dive on react development with axios front end rest api
(lab) deep dive on flask rest api with react with mysql
(lab) deep dive on text summarization inference on web app
(lab) deep dive on bert (llm) fine tunning and emotion analysis on web app
(lab) deep dive on distributed gpu programming with natural language processing (large language models))
(lab) deep dive on generative ai use cases, project lifecycle, and model pre-training
(lab) fine-tuning and evaluating large language models
(lab) reinforcement learning and llm-powered applications, align fine tunning with user feedback
(lab) quantization of large language models with modern nvidia gpu's
(lab) c++ oop tensorrt quantization and fast inference
(lab) deep dive on hugging face library
(lab)translation ● text summarization ● question answering
(lab)sequence-to-sequence models, only encoder based models, only decoder based models
(lab)define the terms generative ai, large language models, prompt, and describe the transformer architecture that powers llms
(lab)discuss computational challenges during model pre-training and determine how to efficiently reduce memory footprint
(lab)describe how fine-tuning with instructions using prompt datasets can improve performance on one or more tasks
(lab)explain how peft decreases computational cost and overcomes catastrophic forgetting
(lab)describe how rlhf uses human feedback to improve the performance and alignment of large language models
(lab)discuss the challenges that llms face with knowledge cut-offs, and explain how information retrieval and augmentation techniques can overcome these challen
Recognize and understand the various strategies and techniques used in fine-tuning language models for specialized applications.
Master the skills necessary to preprocess datasets effectively, ensuring they are in the ideal format for ai training.
Investigate the vast potential of fine-tuned ai models in practical, real-world scenarios across multiple industries.
Acquire knowledge on how to estimate and manage the costs associated with ai model training, making the process efficient and economic
Distributing computing for (ddp) distributed data parallelization and fully shared data parallel across multi gpu/cpu with pytorch together with retrieval augme
The roberta model was proposed in roberta: a robustly optimized bert pretraining approach
Show more
Show less

Syllabus

All course summary

Course Github LINK:

https://github.com/fikratgasimovsoftwareengineer/FullStack_Web_APP/tree/main

React Hooks

Course Overview by Me

React DOM

React Rest API&Axios

Flask Rest API

Javascript Basics Concepts

Javascript Advance concepts

Course Description and what you will learn

Recommed Course - DeepLearning

Some Demos

Please find all projects laying on the repository:

https://github.com/fikratgasimovsoftwareengineer/FullStack_Web_APP

WebApp-Object Detection Demo

Set up Docker Images,Containers, and Visual Code

My Github Profile:
https://github.com/fikratgasimovsoftwareengineer/FullStack_Web_APP

Docker File Configuration

Docker Build and Set Up

How to Run Docker RUN

Configuration of Docker Container with Visual Code

Prepare YoloV7 Fast Precision Server Side

Yolov7 Start Implementation

Yolov7 Server Implementation 2

Yolov7 Server Implementation 3

Yolov7 Server Implementation 4

Yolov7 Server Implementation 5

Yolov7 Server Implementation 6

Flask Server Implementation for High Security Web App

Flask Server Implementation 1

Flask Server Implementation 2

Flask Server Sign In Implementation

Flask Server Registration Implementation

Flask Server with YoloV7 Deep Learning Integration

Flask Server & Yolov7 Integration

Flask Server & Yolov7 Integration part 2

Flask Server & Yolov7 Integration part 3

Flask Server Web APP Design

Flask Server & Web APP design part 1

Flask Web App DL Inference

Flask Web App DL Image Inference

Flow Diagram for Back-End&Front-End

React Web App Inference with Emotion Detection NLP

Custom Web App Emotion Detection, BERT, Hugging FACE, React JS, Flask, MySql

How to start for Prototyping Large Language Model with Web APP and Flask

BERT & Hugging Face Feature Engineering Part 1

Feature Engineering and Preprocessing part 2

Feature Engineering and Preprocessing part 3

Feature Engineering and Preprocessing part 4

Pytorch Dataloader & Hugging Face Framework(Large Language Models)

Dataloader,Hugging Face Integration

Dataloader,Hugging Face Integration Part 2

Dataloader,Hugging Face Integration Part 3

BERT NLP Transformer : Model Freezing

BERT_FINE Part 1

Prepare Training and Validation Step with BERT

Bert Model has been saved within zip folder.

training part 2

train and val part 3

Train&Val successful

React, Flask, Bert Emotion Inference

This is pretrained model zip file that contains both pretrained model and tokenizer.Please, unzip it and locate it in local working directory!

Where we are and where we have to ??

preprocessing setup

Model BackBone setup

Model Inference Part 1

Model Inference Part 2

Flask Server Integration with Model Pretrained

Flask Server & Inference Part 1

Flask Server & Inference Part 2

Flask Server & Inference Part 3

React Development Web App

React Familiarity

React Installation

React set up part 1

react successful installation

Main React Component

Evaluate Implementation

Emotion Analysis component

User FeedBackk Route API

Non User Feedback Route API

Emotion Analysis Implementation Return

Demo Emotion Analysis Successfully Implementated

Question&Answering React WEB and LLM Transformer Based PDF Analizer

Demo Transformer-React

React Question Answer Component

React Question Answer Component 2

LLM Transormer Explanation

Flask Route Based Implementation

CPlus_Cplus TensorRT Tutorial & Demo

CPlus_Cplus TensorRT&Onnx With YoloV4

How to implement Onnx Cplus_cplus with YoloV5 Inference

Deep Dive into Generative AI and Large Language Models PART 1

Generative AI & LLM

LLM use cases and Tasks

Text generation before transformers

Transformer Archiecture Part 1

Transformer Archiecture Part 2

Transform Based-Translation Task

Transform-Encoder-Decoder

Prompt&Prompt Engineering

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Develops skills and knowledge in Generative AI and Large Language models which are key to tackling real world problems in today's tech industry

Taught by PhD Researcher AI & Robotics Scientist Fikrat Gasimov who is recognized for their work in AI and Robotics Science

Examines various state-of-the-art Generative AI models such as The Falcon, LLAMA2, BLOOM, MPT, Vicuna, FLAN-T5, GPT2/GPT3, GPT NEOX

Provides hands-on practice with Tensorflow, Pytorch, Keras models, HuggingFace with Docker Service to gain practical experience with industry-standard tools

Covers how to quantize TensorRT frameworks for deployment in a variety of sectors, enhancing the efficiency and applicability of models

Teaches how to integrate Reinforcement Learning (PPO) to Large Language Models, enabling learners to customize and fine-tune models based on human feedback

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Learn Everything about Full-Stack Generative AI, LLM MODELS with these activities:

Organize Course Materials

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Organize and prepare your learning materials to improve your understanding and engagement with the course.

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Gather all materials, including syllabus, notes, assignments, and readings.
Create a system for organizing and storing your materials.
Review materials on a regular to familiarize yourself with the course content.

Read 'Deep Learning for Coders with Fastai and PyTorch'

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Gain a deeper understanding of deep learning concepts by reading this comprehensive book.

View Deep Learning for Coders with Fastai and... on Amazon

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Read through the book thoroughly, taking notes and highlighting important concepts.
Work through the exercises and examples provided in the book.
Apply the knowledge gained from the book to your own projects.

Join a Study Group

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Enhance your understanding through peer collaboration and support.

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Find or form a study group with fellow classmates or online learners.
Meet regularly to discuss course materials, share perspectives, and work on assignments together.
Engage in group discussions, brainstorming sessions, and peer review.

Five other activities

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Practice Docker Commands

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Reinforce your understanding of Docker commands by completing practice exercises.

Browse courses on Docker

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Review the Docker documentation and tutorials.
Create a simple Dockerfile and build an image.
Run Docker commands to manage containers.
Troubleshoot common Docker issues.
Experiment with different Docker features.

Learn about TensorRT

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Enhance your knowledge of TensorRT by following guided tutorials.

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Find online tutorials or documentation on TensorRT.
Follow the tutorials to install and configure TensorRT.
Learn about TensorRT's key features and capabilities.
Apply TensorRT to your own projects or models.
Engage in online forums or communities to ask questions and learn from others.

Literature on Real-World AI Applications

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Expand your knowledge of real-world AI applications by compiling a collection of relevant resources.

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Search for research papers, articles, and case studies on various industries where AI is being applied.
Organize and categorize the resources based on application area or industry.
Summarize the key findings and insights from the resources.
Share your compilation with classmates or online communities.

Build a Custom Web Application

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Solidify your understanding of web application development by creating your own custom project.

Browse courses on Web Development

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Plan and design your web application.
Choose appropriate technologies and frameworks.
Implement the front-end and back-end components.
Test and debug your application.
Deploy and maintain your application.

Contribute to Open Source AI Projects

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Gain practical experience and contribute to the AI community by working on open source projects.

Browse courses on Open Source

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Identify open source AI projects that align with your interests and skills.
Read the project documentation and familiarize yourself with the codebase.
Start contributing by fixing bugs, implementing new features, or improving documentation.
Engage with the project community through online forums or discussions.

Career center

Learners who complete Learn Everything about Full-Stack Generative AI, LLM MODELS will develop knowledge and skills that may be useful to these careers:

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