Applied Generative AI and Natural Language Processing | Udemy [Update 04/2024]
English | Size: 3.7 GB
Genre: eLearning[/center]

Understand Generative AI, Prompt Engineering, Huggingface-Models, LLMs, Vector Databases, RAG, OpenAI, Claude, Llama2

What you'll learn
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 Promping
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-Critique
Claude 3
Open Source Models, e.g. LLama 2, Mistral

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 integrating it in 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-Critique

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

Meta Llama 2

Mistral Mixtral

Data Augmentation

Theory and Approaches of NLP Data Augmentation

Implementation of Data Augmentation

Miscellanious

Claude 3

Tools and LLM-Function

Who this course is for:
Developers who want to apply NLP-models

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