Web Applications with Large Language Model Fast Inference

Free Download Web Applications with Large Language Model Fast Inference
Published 4/2024
Created by PhD Researcher AI & Robotics Scientist Fikrat Gasimov
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English | Duration: 72 Lectures ( 8h 54m ) | Size: 5.67 GB

Python, Flask, C++, jаvascript, Natural Language Processing, BootStrap DashBoards for 20X Fast Inference Prototypes
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
Requirements:
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
Description:
This course is mainly considered for any candidates(students, engineers,experts) that have great motivation to learn deep learning model training and deeployment with Python Based and jаvascript Web Applications, as well as with C/C++ Programming Languages. Candidates will have deep knowledge of docker, and usage of tensorflow ,pytorch, keras models with docker. In addition, they will be able to optimize and optimizer TensorRT frameworks for deployment in variety of sectors. Moreover, They will learn deployment of quantized model to Web Pages developed with React, jаvascript and FLASK Here you will also learn how to integrate Reinforcement Learning to Large Language Model, in order to fine them with Human Feedback based. Candidates will learn to code and debug in C/C++ Programming languages at least in intermediate level.Learning 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 Areas
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, jаvascript, Python
Homepage

https://www.udemy.com/course/web-applications-with-large-language-model-fast-inference/

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