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[udemy] Applied Generative AI and Natural Language Processing
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English + srt | Duration: 117 Lectures ( 9h ) | Size: 4.12 GB
Understand Generative AI, Prompt-Engineering, implement Huggingface-Models,
LLMs, Vector Databases, RAG, and more
You will learn that
Introduction to Natural Language Processing (NLP)
model implementation based on huggingface models
working with OpenAI
Vector databases
Multimodal Vector Databases
Retrieval Augmented Generation (RAG)
Real-World Applications and Case Studies
implement Zero-Shot Classification, Text Classification, Text Generation
fine-tune models
data augmentation
Prompt engineering
Zero shot prompting
Few-Shot Prompting
Chain of Thought (Few-Shot CoT, Zero-Shot CoT)
Self-Consistency Chain of Thought
Prompt chaining
Tree of Thought
Self-feedback
Self criticism
Claude 3
Open Source Models, eg LLama 2, Mistral
Requirements
Python Basic knowledge
Basic knowledge on How Deeplearning works
Who is this course suitable for:
Developers who want to apply NLP models
Course Content
Download
FreeDL
UsersDrive
ClicknUpload
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English + srt | Duration: 117 Lectures ( 9h ) | Size: 4.12 GB
Understand Generative AI, Prompt-Engineering, implement Huggingface-Models,
LLMs, Vector Databases, RAG, and more
You will learn that
Introduction to Natural Language Processing (NLP)
model implementation based on huggingface models
working with OpenAI
Vector databases
Multimodal Vector Databases
Retrieval Augmented Generation (RAG)
Real-World Applications and Case Studies
implement Zero-Shot Classification, Text Classification, Text Generation
fine-tune models
data augmentation
Prompt engineering
Zero shot prompting
Few-Shot Prompting
Chain of Thought (Few-Shot CoT, Zero-Shot CoT)
Self-Consistency Chain of Thought
Prompt chaining
Tree of Thought
Self-feedback
Self criticism
Claude 3
Open Source Models, eg LLama 2, Mistral
Requirements
Python Basic knowledge
Basic knowledge on How Deeplearning works
Join my comprehensive course on Natural Language Processing (NLP).
The course is designed for both beginners and seasoned professionals.
This course is your gateway to unlocking the immense potential of NLP and Generative AI in solving real-world challenges.
It covers a wide range of different topics and brings you up to speed on implementing NLP solutions.
Course Highlights:
NLP Introduction
Gain a solid understanding of the fundamental principles
that govern Natural Language Processing and its applications.
Basics of NLP
Word Embeddings
Transformers
Apply Huggingface for Pre-Trained Networks
Learn about Huggingface models and how to apply them to your needs
Model fine tuning
Sometimes pre-trained networks are not sufficient,
so you need to fine-tune an existing model on your specific task and / or dataset.
In this section you will learn how.
Vector databases
Vector Databases make it simple to query information from texts.
You will learn how they work and how to implement vector databases.
Tokenization
Implement Vector DB with ChromaDB
Multimodal Vector DB
OpenAI API
OpenAI with ChatGPT provides a very powerful tool for NLP.
You will learn how to make use of it via Python and integrate it into your workflow.
Prompt engineering
Learn strategies to create efficient prompts
Advanced Prompt Engineering
Few-Shot Prompting
Chain of Thought
Self-Consistency Chain of Thought
Prompt chaining
Reflection
Tree of Thought
Self-feedback
Self criticism
Retrieval Augmented Generation
RAG theory
Implement RAG
Capstone Project “Chatbot”
create a chatbot to "chat" with a PDF document
create a web application for the chatbot
Open source LLMs
learn how to use OpenSource LLMs
Data Augmentation
Theory and Approaches of NLP Data Augmentation
Implementation of Data Augmentation
The course is designed for both beginners and seasoned professionals.
This course is your gateway to unlocking the immense potential of NLP and Generative AI in solving real-world challenges.
It covers a wide range of different topics and brings you up to speed on implementing NLP solutions.
Course Highlights:
NLP Introduction
Gain a solid understanding of the fundamental principles
that govern Natural Language Processing and its applications.
Basics of NLP
Word Embeddings
Transformers
Apply Huggingface for Pre-Trained Networks
Learn about Huggingface models and how to apply them to your needs
Model fine tuning
Sometimes pre-trained networks are not sufficient,
so you need to fine-tune an existing model on your specific task and / or dataset.
In this section you will learn how.
Vector databases
Vector Databases make it simple to query information from texts.
You will learn how they work and how to implement vector databases.
Tokenization
Implement Vector DB with ChromaDB
Multimodal Vector DB
OpenAI API
OpenAI with ChatGPT provides a very powerful tool for NLP.
You will learn how to make use of it via Python and integrate it into your workflow.
Prompt engineering
Learn strategies to create efficient prompts
Advanced Prompt Engineering
Few-Shot Prompting
Chain of Thought
Self-Consistency Chain of Thought
Prompt chaining
Reflection
Tree of Thought
Self-feedback
Self criticism
Retrieval Augmented Generation
RAG theory
Implement RAG
Capstone Project “Chatbot”
create a chatbot to "chat" with a PDF document
create a web application for the chatbot
Open source LLMs
learn how to use OpenSource LLMs
Data Augmentation
Theory and Approaches of NLP Data Augmentation
Implementation of Data Augmentation
Who is this course suitable for:
Developers who want to apply NLP models
Course Content
Download
FreeDL
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UsersDrive
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ClicknUpload
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