Generative AI, LLM MODELS, Full Stack 15+ Projects

Focused View

Fikrat Gasimov

8:54:45

286 View

1. All course summary

1.1 webapp.pptx

1. Course Summary.mp4

06:29

2. React Hooks.mp4

06:44

3. React DOM.mp4

07:36

4. React Rest API&Axios.mp4

07:41

5. Flask Rest API.mp4

04:34

6. Javascript Basics Concepts.mp4

07:05

7. Javascript Advance concepts.mp4

07:52

2. Some Demos

1. WebApp-Object Detection Demo.mp4

05:14

2. YoloV7 Fast Inference Demo.mp4

00:52

3. Set up Docker Images,Containers, and Visual Code

1.1 wepapp_state_diagram.drawio.zip

1. Overall Flow State Diagram for Inference Web APP.mp4

02:44

2. Docker File Configuration.mp4

08:00

3. Docker Build and Set Up.mp4

02:07

4. How to Run Docker RUN.mp4

07:59

5. Configuration of Docker Container with Visual Code.mp4

03:56

4. Prepare YoloV7 Fast Precision Server Side

1. Yolov7 Start Implementation.mp4

03:11

2. Yolov7 Server Implementation 2.mp4

11:09

3. Yolov7 Server Implementation 3.mp4

09:46

4. Yolov7 Server Implementation 4.mp4

12:14

5. Yolov7 Server Implementation 5.mp4

12:53

6. Yolov7 Server Implementation 6.mp4

07:50

5. Flask Server Implementation for High Security Web App

1. Flask Server Implementation 1.mp4

16:40

2. Flask Server Implementation 2.mp4

10:00

3. Flask Server Sign In Implementation.mp4

09:57

4. Flask Server Registration Implementation.mp4

06:57

6. Flask Server with YoloV7 Deep Learning Integration

1.1 images.zip

1.2 Images.zip

1.3 models.zip

1. Flask Server & Yolov7 Integration.mp4

13:33

2. Flask Server & Yolov7 Integration part 2.mp4

05:37

3. Flask Server & Yolov7 Integration part 3.mp4

04:52

7. Flask Server Web APP Design

1. Flask Server & Web APP design part 1.mp4

13:54

2. Flask Web App DL Inference.mp4

09:54

3. Flask Web App DL Image Inference.mp4

07:43

4.1 flow diagram for back-end&front-end.drawio.zip

4. Flow Diagram for Back-End&Front-End.mp4

03:40

8. React Web App Inference with Emotion Detection NLP

1.1 inference web app.drawio.zip

1. Custom Web App Emotion Detection, BERT, Hugging FACE, React JS, Flask, MySql.mp4

04:55

2.1 cleaning_dataset.drawio.zip

2. How to start for Prototyping Large Language Model with Web APP and Flask.mp4

03:14

3. BERT & Hugging Face Feature Engineering Part 1.mp4

10:51

4. Feature Engineering and Preprocessing part 2.mp4

12:27

5. Feature Engineering and Preprocessing part 3.mp4

08:24

6. Feature Engineering and Preprocessing part 4.mp4

04:08

9. Pytorch Dataloader & Hugging Face Framework(Large Language Models)

1. Dataloader,Hugging Face Integration.mp4

04:45

2. Dataloader,Hugging Face Integration Part 2.mp4

11:52

3. Dataloader,Hugging Face Integration Part 3.mp4

15:40

10. BERT NLP Transformer Model Freezing

1. BERT_FINE Part 1.mp4

12:58

11. Prepare Training and Validation Step with BERT

1.1 models.zip

1. Bert Model Train&Val part 1.mp4

07:41

2. training part 2.mp4

07:35

3. train and val part 3.mp4

16:45

4. Train&Val successful.mp4

04:32

12. React, Flask, Bert Emotion Inference

1.1 pretrained.zip

1. Pretrained Model Bert and Tokenizer download.mp4

02:07

2. Where we are and where we have to .mp4

02:31

3. preprocessing setup.mp4

05:16

4. Model BackBone setup.mp4

02:49

5. Model Inference Part 1.mp4

06:48

6. Model Inference Part 2.mp4

05:11

13. Flask Server Integration with Model Pretrained

1. Flask Server & Inference Part 1.mp4

04:09

2. Flask Server & Inference Part 2.mp4

10:10

3. Flask Server & Inference Part 3.mp4

07:57

14. React Development Web App

1. React Familiarity.mp4

07:47

2. React Installation.mp4

05:30

3. React set up part 1.mp4

01:58

4.1 emotion_detection.zip

4.2 question-answering.zip

4.3 questions.zip

4. react successful installation.mp4

04:46

5. Main React Component.mp4

22:33

6. Evaluate Implementation.mp4

04:21

7. Emotion Analysis component.mp4

07:32

8. User FeedBackk Route API.mp4

10:35

9. Non User Feedback Route API.mp4

07:02

10. Emotion Analysis Implementation Return.mp4

11:51

11. Demo Emotion Analysis Successfully Implementated.mp4

08:47

15. LLM Transformer Based PDF Analizer with React

1.1 presentation.zip

1. Demo Transformer-React.mp4

01:52

2. React Question Answer Component.mp4

09:43

3. React Question Answer Component 2.mp4

04:04

4. LLM Transormer Explanation.mp4

04:55

5. Flask Route Based Implementation.mp4

05:29

16. CPlus_Cplus TensorRT Tutorial & Demo

1. CPlus_Cplus TensorRT&Onnx With YoloV4.mp4

02:45

2. How to implement Onnx Cplus_cplus with YoloV5 Inference.mp4

01:47

Description

Core Practical Generative AI, LLM, Javascript Applications for 20X Fast Inference Prototypes. Get Hired: Generative AI

What You'll Learn?

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 BERT (LLM) Fine tunning and Emotion Analysis on Web App
(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

Who is this for?

University Students

New Graduates

Workers

Those want to deploy Deep Learning Models on Edge Devices.

AI experts

Embedded Software Engineer

Natural Language Developers

Machine Learning & Deep Learning Engineerings

Full Stack Developers, Javascript, Python

What You Need to Know?

In order to understand this course, candidates needs follows basically course of : Tensorflow-Pytorch-TensorRT-ONNX-From Zero to Hero(YOLOVX.

Basic C++ programming Knowledge

Basic C Programming Knowledge

Local Nvidia GPU Device

Basic Natural Language Processing Knowledge

Basic Python Knowledge

Basic HTML, CSS, BootStrap Knowledge

More details

Description
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 theirÂ deeployment with Python Based and Javascript Web Applications, as well as with C/C++ Programming Languages. Candidates will have deep knowledge onÂ TensorFlow , Pytorch,Â 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 FLASK
Here you will also learn howÂ to integrate Reinforcement Learning(PPO) to Large Language Model, in order to fine them with Human Feedback based.Â
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, GPTÂ NEOX
BERT 101, Distil BERT
FINE-Tuning Small Models under supervision of BIG Models
and soo onn...

Learning and Installation of Docker from scratch
Knowledge of Javscript, HTML ,CSS, Bootstrap
React Hook,Â DOM and Javacscript Web Development
Deep Dive on Deep Learning Transformer based Natural Language Processing
Python FLASKÂ Rest API along with MySql
Preparation of DockerFiles, Docker Compose as well as Docker Compose Debug file
Configuration and Installation of Plugin packages in Visual Studio Code
Learning, Installation and Confguration of frameworks such as Tensorflow, Pytorch, Kears with docker images from scratch
Preprocessing and Preparation of Deep learning datasets for training and testing
OpenCVÂ DNNÂ with C++ Inference
Training, Testing and Validation of Deep Learning frameworks
Conversion of prebuilt models to OnnxÂ and Onnx Inference on images with C++ Programming
Conversion of onnx model to TensorRT engine with C++ RunTime and Compile Time API
TensorRT engine Inference on images and videos
Comparison of achieved metrices and result between TensorRT and Onnx Inference
Prepare 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 CloudÂ Areas
Large Language Models Fine Tunning
Large Language Models Hands-On-Practice:Â BLOOM, GPT3-GPT3.5, FLAN-T5 family
Large Language Models Training, Evaluation and User-Defined Prompt IN-Context Learning/On-Line Learning
Human FeedBack Alignment on LLM with Reinforcement Learning (PPO) with Large Language Model : BERT and FLAN-T5
How 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 techniques

Importante Note:
Â Â Â In this course, there is not nothing to copy &Â 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.

Who this course is for:
University Students
New Graduates
Workers
Those want to deploy Deep Learning Models on Edge Devices.
AI experts
Embedded Software Engineer
Natural Language Developers
Machine Learning & Deep Learning Engineerings
Full Stack Developers, Javascript, Python

User Reviews

Rating

average 0

Total votes0

Focused display

I am Fikrat Gasimov. I am Full Stack C/C++ Qt-QMl, Embedded Software Developer as well as AI Expert. My first experience was as a Deep Learning researcher in autonomous driving cars. I have been working on diverse sectors, such as developing algorithms as well as Apps for Android and IoS mobile devices. In addition, I have been working on diversi mobile robots such as unitree, japanese mobile robots , making them fully automous , apart from that, i am prototyping my own drones interms of software and hardware. Moreover, I am developing drivers for edge devices for bootloaders and camera servos, as well deep learning algorithms, ground control stations to control them remotely.I have 5 years of experience in sectors, such as Unity 3D, Automotive, AI & Machine Learning, Drones, as well as Cloud Systems.

Udemy

View courses Udemy

Students take courses primarily to improve job-related skills.Some courses generate credit toward technical certification. Udemy has made a special effort to attract corporate trainers seeking to create coursework for employees of their company.