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Foundations of Large Language Models: Beginner NLP Course

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Foundations of Large Language Models: Beginner NLP Course, available at Free, has an average rating of 3.64, with 78 lectures, based on 33 reviews, and has 897 subscribers.

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You will learn about Gain insight into large language models (LLMs), their architecture, training, and applications in NLP tasks for practical implementation. Acquire hands-on experience in fine-tuning pre-trained LLMs for specific NLP applications like text generation, sentiment analysis, and translation. Master advanced techniques for optimizing LLM performance, including hyperparameter tuning, transfer learning, and model interpretation methods. Master using OpenAI's GPT series & Hugging Face's Transformers to solve NLP challenges effectively in real-world scenarios. This course is ideal for individuals who are Data scientists, NLP enthusiasts, developers, and anyone interested in mastering large language models (LLMs) and advancing their skills in natural language processing (NLP) applications It is particularly useful for Data scientists, NLP enthusiasts, developers, and anyone interested in mastering large language models (LLMs) and advancing their skills in natural language processing (NLP) applications.

Enroll now: Foundations of Large Language Models: Beginner NLP Course

Summary

Title: Foundations of Large Language Models: Beginner NLP Course

Price: Free

Average Rating: 3.64

Number of Lectures: 78

Number of Published Lectures: 78

Number of Curriculum Items: 78

Number of Published Curriculum Objects: 78

Original Price: Free

Quality Status: approved

Status: Live

What You Will Learn

  • Gain insight into large language models (LLMs), their architecture, training, and applications in NLP tasks for practical implementation.
  • Acquire hands-on experience in fine-tuning pre-trained LLMs for specific NLP applications like text generation, sentiment analysis, and translation.
  • Master advanced techniques for optimizing LLM performance, including hyperparameter tuning, transfer learning, and model interpretation methods.
  • Master using OpenAI's GPT series & Hugging Face's Transformers to solve NLP challenges effectively in real-world scenarios.

Who Should Attend

  • Data scientists, NLP enthusiasts, developers, and anyone interested in mastering large language models (LLMs) and advancing their skills in natural language processing (NLP) applications

Target Audiences

  • Data scientists, NLP enthusiasts, developers, and anyone interested in mastering large language models (LLMs) and advancing their skills in natural language processing (NLP) applications

Welcome to the “Beginners Guide to Learn Large Language Models” course, your comprehensive journey into the fascinating world of large language models (LLMs) and their transformative applications in natural language processing (NLP). Whether you’re a budding data scientist, developer, or NLP enthusiast, this course is tailored to equip you with the fundamental knowledge and practical skills needed to harness the power of LLMs and revolutionize your approach to NLP tasks.

Introduction to Large Language Models: We kick off the course with an exploration of the fundamentals of large language models, tracing their evolution, and delving into their wide-ranging applications, from text generation to sentiment analysis and beyond.

Neural Networks and Deep Learning: Next, we dive into the basics of neural networks and deep learning, laying the groundwork for understanding how LLMs process and generate natural language. We cover essential topics such as preprocessing text data, tokenization, and the importance of stemming or lemmatization.

Transformer Architecture: In this module, we dissect the revolutionary transformer architecture, the backbone of modern LLMs. We unravel the self-attention mechanism and explore how transformers are trained and fine-tuned for specific NLP tasks.

Popular Large Language Models – GPT, BERT: Discover two of the most influential LLMs – GPT and BERT – and examine their architectures, capabilities, and real-world applications through hands-on demonstrations.

Fine-tuning Pre-trained Models: Learn the art of transfer learning with pre-trained language models, enabling you to adapt and fine-tune them for specialized NLP tasks. Explore the benefits, challenges, and considerations of transfer learning with practical demonstrations.

Application of LLMs with Samples: Uncover the myriad applications of LLMs in NLP, including text generation, classification, question answering, and machine translation. Gain insights from real-world examples and demonstrations showcasing the versatility of LLMs.

LLMs for Natural Language Processing with Samples: Delve deeper into the role of LLMs in NLP tasks such as sentiment analysis, named entity recognition, and text summarization. Explore the benefits, challenges, and ethical considerations associated with deploying LLMs in real-world scenarios.

LLMs for Speech Recognition and Synthesis: Explore how LLMs are reshaping speech recognition, synthesis, and conversational AI. Examine the challenges, limitations, and ethical considerations surrounding the use of LLMs in speech-related applications.

OpenAI: GPT-3 – Use cases: Finally, we peer into the future of large language models, with a focus on OpenAI’s GPT-3 and its groundbreaking use cases in language translation, text generation, and beyond. Gain practical tips and resources for leveraging LLMs effectively in your projects.

Embark on this enriching journey and unlock the full potential of large language models in transforming the landscape of natural language processing. Enroll now and embark on your path to becoming an LLM expert!

Course Curriculum

Chapter 1: Introduction to Large Language Models

Lecture 1: Introduction to Large Language Models

Lecture 2: History and Evolution of Language Models

Lecture 3: Applications of Large Language Models

Lecture 4: Text Generation

Lecture 5: Language Translation

Lecture 6: Sentiment Analysis

Lecture 7: Chatbots

Lecture 8: Question-Answering Systems

Lecture 9: Code Generation

Lecture 10: Conclusion

Chapter 2: Neural Networks and Deep Learning

Lecture 1: Introduction

Lecture 2: Understanding Neural Networks and Deep Learning

Lecture 3: Basics of Natural Language Processing

Lecture 4: Preprocessing Text Data for Language Models

Lecture 5: Tokenization Example

Lecture 6: Removing Stop Words

Lecture 7: Removing Stop Words Example

Lecture 8: Stemming or Lemmatization

Lecture 9: Lemmatization Example

Lecture 10: Conclusion

Chapter 3: Transformer Architecture

Lecture 1: Introduction

Lecture 2: Transformer Architecture

Lecture 3: Transformer Architecture for Language Models

Lecture 4: Self-Attention Mechanism

Lecture 5: Attention Mechanism in Language Models

Lecture 6: Training Language Models

Lecture 7: Fine-tuning Language Models

Lecture 8: Transformer Architecture – Demo

Lecture 9: Self-Attention Mechanism – Demo

Lecture 10: Conclusion

Chapter 4: Popular Large Language Models – GPT, BERT

Lecture 1: Introduction

Lecture 2: GPT Models

Lecture 3: BERT Models

Lecture 4: GPT Model Architecture

Lecture 5: BERT Model Architecture

Lecture 6: GPT – Demo

Lecture 7: BERT – Demo

Lecture 8: Conclusion

Chapter 5: Fine-tuning Pre-trained Models

Lecture 1: Introduction

Lecture 2: Transfer Learning with Language Models

Lecture 3: Fine-tuning Language Models for Specific Tasks

Lecture 4: Fine-tuning a Language Model – Demo

Lecture 5: Adapting Language Models for Multilingual Applications

Lecture 6: Multilingual Language Model – Demo

Lecture 7: Benefits of Transfer Learning with Language Models

Lecture 8: Challenges and Considerations

Lecture 9: Conclusion

Chapter 6: Application of LLMs with Samples

Lecture 1: Introduction

Lecture 2: Large Language Models for Text Generation

Lecture 3: Text Generation with GPT-3 – Demo

Lecture 4: Large Language Models for Text Classification

Lecture 5: Text Classification with BERT – Demo

Lecture 6: Large Language Models for Question Answering

Lecture 7: Question Answering with T5 – Demo

Lecture 8: Conclusion

Chapter 7: LLMs for Natural Language Processing with Samples

Lecture 1: Introduction

Lecture 2: Large Language Models for Machine Translation

Lecture 3: Large Language Models for Sentiment Analysis

Lecture 4: Large Language Models for Named Entity Recognition

Lecture 5: Benefits of Large Language Models

Lecture 6: Challenges and Limitations

Lecture 7: Ethical Considerations

Lecture 8: Future Directions

Lecture 9: Conclusion

Chapter 8: LLMs for Speech Recognition and Synthesis

Lecture 1: Introduction

Lecture 2: Large Language Models for Text Summarization

Lecture 3: Large Language Models for Chatbots and Conversational AI

Lecture 4: Large Language Models for Speech Recognition

Lecture 5: Benefits of Large Language Models

Lecture 6: Conclusion

Chapter 9: OpenAI : GPT-3 – Use cases

Lecture 1: Introduction

Lecture 2: Future Trends and Advances in Large Language Models

Lecture 3: OpenAI's GPT-3 and Beyond

Lecture 4: GPT-3 Use Case – Language Translation

Lecture 5: GPT-3 Use Case – Text Generation

Lecture 6: Practical Tips and Resources for Working with Large Language Models

Lecture 7: Resources for Working with Large Language Models

Lecture 8: Conclusion

Instructors

Rating Distribution

  • 1 stars: 4 votes
  • 2 stars: 2 votes
  • 3 stars: 6 votes
  • 4 stars: 9 votes
  • 5 stars: 12 votes

Frequently Asked Questions


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You can view and review the lecture materials indefinitely, like an on-demand channel.


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Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!

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