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Exploring The Technologies Behind ChatGPT, GPT 4 & LLMs

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Exploring The Technologies Behind ChatGPT, GPT 4 & LLMs, available at $54.99, has an average rating of 4.34, with 85 lectures, based on 412 reviews, and has 3467 subscribers.

You will learn about Identify and select the most suitable transformer-based model for specific NLP tasks. Comprehend how transformers process text and generate predictions. Fine-tune transformer-based models with custom datasets. Develop and implement actionable pipelines using fine-tuned models. Deploy fine-tuned models for production use. Perform effective prompt engineering for optimal outputs from GPT-4 and ChatGPT. Understand the concepts of attention mechanisms and their application in NLP. Grasp the principles of transfer learning and its role in NLP. Utilize BERT for natural language understanding tasks. Conduct pre-training and fine-tuning of BERT models. Apply hands-on experience with BERT for various NLP tasks. Explore natural language generation using GPT models. Gain practical experience with GPT models for text generation tasks. Integrate BERT and GPT models for advanced NLP applications. Understand the fundamentals and applications of the T5 model. Engage in hands-on projects with T5 for different NLP tasks. Deploy transformer models in real-world scenarios. Utilize massively large language models effectively. Apply best practices and strategies for using ChatGPT and other LLMs in various applications. This course is ideal for individuals who are Anyone interested in ChatGPT or Anyone interested in Reinforcement Learning from Human Feedback (RLHF) or Aspiring data scientists, machine learning engineers, and NLP practitioners or Anyone who want to gain a solid understanding of transformer models and their applications in modern natural language processing tasks. or Software developers seeking to enhance their productivity and leverage the power of NLP models for innovative applications in their work. It is particularly useful for Anyone interested in ChatGPT or Anyone interested in Reinforcement Learning from Human Feedback (RLHF) or Aspiring data scientists, machine learning engineers, and NLP practitioners or Anyone who want to gain a solid understanding of transformer models and their applications in modern natural language processing tasks. or Software developers seeking to enhance their productivity and leverage the power of NLP models for innovative applications in their work.

Enroll now: Exploring The Technologies Behind ChatGPT, GPT 4 & LLMs

Summary

Title: Exploring The Technologies Behind ChatGPT, GPT 4 & LLMs

Price: $54.99

Average Rating: 4.34

Number of Lectures: 85

Number of Published Lectures: 84

Number of Curriculum Items: 85

Number of Published Curriculum Objects: 84

Original Price: $199.99

Quality Status: approved

Status: Live

What You Will Learn

  • Identify and select the most suitable transformer-based model for specific NLP tasks.
  • Comprehend how transformers process text and generate predictions.
  • Fine-tune transformer-based models with custom datasets.
  • Develop and implement actionable pipelines using fine-tuned models.
  • Deploy fine-tuned models for production use.
  • Perform effective prompt engineering for optimal outputs from GPT-4 and ChatGPT.
  • Understand the concepts of attention mechanisms and their application in NLP.
  • Grasp the principles of transfer learning and its role in NLP.
  • Utilize BERT for natural language understanding tasks.
  • Conduct pre-training and fine-tuning of BERT models.
  • Apply hands-on experience with BERT for various NLP tasks.
  • Explore natural language generation using GPT models.
  • Gain practical experience with GPT models for text generation tasks.
  • Integrate BERT and GPT models for advanced NLP applications.
  • Understand the fundamentals and applications of the T5 model.
  • Engage in hands-on projects with T5 for different NLP tasks.
  • Deploy transformer models in real-world scenarios.
  • Utilize massively large language models effectively.
  • Apply best practices and strategies for using ChatGPT and other LLMs in various applications.

Who Should Attend

  • Anyone interested in ChatGPT
  • Anyone interested in Reinforcement Learning from Human Feedback (RLHF)
  • Aspiring data scientists, machine learning engineers, and NLP practitioners
  • Anyone who want to gain a solid understanding of transformer models and their applications in modern natural language processing tasks.
  • Software developers seeking to enhance their productivity and leverage the power of NLP models for innovative applications in their work.

Target Audiences

  • Anyone interested in ChatGPT
  • Anyone interested in Reinforcement Learning from Human Feedback (RLHF)
  • Aspiring data scientists, machine learning engineers, and NLP practitioners
  • Anyone who want to gain a solid understanding of transformer models and their applications in modern natural language processing tasks.
  • Software developers seeking to enhance their productivity and leverage the power of NLP models for innovative applications in their work.

Notice: As of Aug 7, 2024, this course has undergone a comprehensive revision. Please be assured that it will continue to receive regular updates to maintain its relevance and effectiveness.

Welcome to “Exploring The Technologies Behind ChatGPT, GPT-4 & LLMs” – a meticulously designed course for learners at all levels, offering an unparalleled journey into the transformative world of Large Language Models (LLMs) and their profound impact on modern Natural Language Processing (NLP).

Unlock the Power of Large Language Models

In recent years, the advent of LLMs has revolutionized NLP, showcasing models like BERT, T5, and ChatGPT, which have achieved groundbreaking performance across diverse NLP tasks. From text classification to machine translation, these models have set new standards. However, leveraging their full potential can be challenging due to their complexity and scale. This course demystifies LLMs, providing you with the knowledge and skills to effectively utilize and optimize these models for your specific needs.

Comprehensive Curriculum

Our course covers a broad spectrum of topics essential for mastering LLMs:

  1. Foundations of Large Language Models:

    • Introduction to LLMs and their significance in NLP.

    • Detailed exploration of transformer models and attention mechanisms.

    • Historical context and evolution of NLP technologies.

  2. Core Techniques and Concepts:

    • Understanding how transformers process text and generate predictions.

    • Deep dive into attention mechanisms and their role in transformers.

    • Practical insights into BERT, Wordpiece tokenization, and embeddings.

  3. Advanced Topics:

    • Fine-tuning GPT-4 with custom examples.

    • Techniques for prompt engineering to achieve optimal outputs from GPT-4 and ChatGPT.

    • Building and deploying custom LLM applications.

  4. Hands-On Learning:

    • Practical exercises using PyTorch for fine-tuning transformers.

    • Hands-on projects with BERT, GPT, and T5 models.

    • Real-world applications and use cases, such as sentiment analysis, text summarization, and conversational AI.

  5. Specialized Topics:

    • Transfer learning and its application in NLP.

    • Pre-training and fine-tuning BERT for specific tasks.

    • Exploration of the Vision Transformer for integrating vision and language tasks.

  6. Deployment and Optimization:

    • Creating actionable pipelines using fine-tuned models.

    • Strategies for deploying models in production environments.

    • Practical tips for optimizing LLM performance and scalability.

Why Enroll?

By the end of this course, you will have a deep understanding of GPT-4 architecture and the ChatGPT system. You will gain hands-on experience with state-of-the-art models and techniques, enabling you to:

  • Recognize the best transformer-based model for various NLP tasks.

  • Fine-tune and deploy models with custom data.

  • Engineer prompts for precise and useful outputs.

  • Apply advanced NLP techniques to real-world problems.

Course Highlights

  • Expert Guidance: Learn from an experienced instructor with a wealth of knowledge in NLP and LLMs.

  • Interactive Learning: Engage with visually captivating lectures, solved mathematical examples, and practical Python exercises in Jupyter notebooks.

  • Real-World Applications: Discover the power of NLP through real-world examples and use cases that demonstrate the practical benefits of LLM techniques.

  • Cutting-Edge Content: Stay ahead in the rapidly evolving field of NLP with the latest advancements and best practices.

Join Us Today

Enroll in “Exploring The Technologies Behind ChatGPT, GPT-4 & LLMs” and embark on a transformative journey through the captivating world of NLP. Gain the skills and knowledge to revolutionize your NLP projects and stay at the forefront of this dynamic field. Whether you are a seasoned practitioner or a curious newcomer, our comprehensive course offers the step-by-step guidance and interactive learning experience you need to master GPT-4, ChatGPT, and beyond.

Enroll now and take the first step towards mastering the future of NLP!

Course Curriculum

Chapter 1: Welcome

Lecture 1: Introduction to the Course

Lecture 2: Welcome Message

Chapter 2: Getting Started with Large Language Models

Lecture 1: Understanding Large Language Models: GPT, Gemini, Claude, and More

Lecture 2: The Evolution of Natural Language Processing Techniques

Lecture 3: Understanding Attention Mechanisms in Deep Learning

Lecture 4: Fundamentals of Encoder-Decoder Architectures

Lecture 5: Introduction to Transformer Models

Lecture 6: Detailed Exploration of Transformer Mechanics

Lecture 7: Deep Dive into Scaled Dot Product Attention – Part 1

Lecture 8: Understanding Transformers and Attention in NLP (Updated)

Lecture 9: Deep Dive into Scaled Dot Product Attention – Part 2

Lecture 10: Deep Dive into Scaled Dot Product Attention – Part 3

Lecture 11: Comprehensive Study of Multi-Headed Attention – Part 1

Lecture 12: Comprehensive Study of Multi-Headed Attention – Part 2

Lecture 13: Exploring Transfer Learning Techniques – Part 1

Lecture 14: Exploring Transfer Learning Techniques – Part 2

Lecture 15: Getting Started with PyTorch for NLP

Lecture 16: Streamlining Transformer Models with PyTorch

Lecture 17: Introduction to BERT: The New NLP Paradigm – Part 1

Lecture 18: Introduction to BERT: The New NLP Paradigm – Part 2

Lecture 19: Techniques for Effective Wordpiece Tokenization – Part 1

Lecture 20: Techniques for Effective Wordpiece Tokenization – Part 2

Lecture 21: Understanding BERT's Embedding Capabilities – Part 1

Lecture 22: Understanding BERT's Embedding Capabilities – Part 2

Lecture 23: Advanced Language Modeling with BERT – Part 1

Lecture 24: Advanced Language Modeling with BERT – Part 2

Lecture 25: Next Sentence Prediction Techniques with BERT – Part 1

Lecture 26: Next Sentence Prediction Techniques with BERT – Part 2

Lecture 27: Advanced Applications of BERT in NLP Tasks – Part 1

Lecture 28: Advanced Applications of BERT in NLP Tasks – Part 2

Lecture 29: An Overview of BERT Variants

Lecture 30: BERT Variants: Advanced Concepts

Lecture 31: Basics of Sequence Classification with BERT

Lecture 32: Advanced Sequence Classification Techniques with BERT

Lecture 33: Practical Applications of BERT in Sequence Classification

Lecture 34: Foundations of Token Classification with BERT

Lecture 35: Advanced Token Classification Strategies Using BERT

Lecture 36: Core Concepts of Question Answering with BERT

Lecture 37: In-Depth Analysis of BERT for Question Answering

Lecture 38: GPT Model Architecture: An Introduction

Lecture 39: Innovative Applications of the GPT Architecture

Lecture 40: Techniques in Masked Multi-Head Attention

Lecture 41: Advanced Uses of Masked Multi-Head Attention

Lecture 42: Practical Implementations of Multi-Head Attention

Lecture 43: Strategies for Efficient GPT Pre-Training

Lecture 44: Optimizing GPT for Enhanced Performance

Lecture 45: Introduction to Few-Shot Learning with GPT

Lecture 46: Advanced Few-Shot Learning Techniques

Lecture 47: Basics of Stylistic Completion with GPT

Lecture 48: Advanced Stylistic Completion Techniques with GPT

Lecture 49: Implementing Efficient Code Dictation with GPT

Lecture 50: Mastering GPT for Code Dictation: Deep Dive

Lecture 51: GPT for Code Dictation: Advanced Implementation and Strategies

Lecture 52: Exploring Siamese BERT Networks for Semantic Analysis

Lecture 53: Deep Dive into Siamese BERT Networks for Semantic Analysis

Lecture 54: Advanced Siamese BERT Networks for Semantic Interpretation

Lecture 55: Siamese BERT Networks: Semantic Interpretation Case Studies

Lecture 56: Multitasking with GPT: An Introductory Approach

Lecture 57: Advancing Multi-Task Performance in GPT

Lecture 58: Practical Applications of Multitasking with GPT

Lecture 59: Introduction to T5's Encoder-Decoder Architecture

Lecture 60: Principles and Techniques of Pre-training the T5 Model

Lecture 61: Understanding Cross-Attention Mechanisms in T5

Lecture 62: Exploring Pre-Trained T5 Models: Fundamentals

Lecture 63: Advanced Applications of Pre-Trained T5 Models

Lecture 64: Implementing Abstractive Summarization with T5: Core Techniques

Lecture 65: Enhancing Techniques in Abstractive Summarization with T5

Chapter 3: Course materials – Notebooks & Data

Lecture 1: Accessing and Utilizing Course Working Files

Chapter 4: Introduction to Reinforcement Learning

Lecture 1: Exploring RLHF's Role in Advancing ChatGPT

Lecture 2: Fundamental Principles of Reinforcement Learning

Lecture 3: Mastering OpenAI Software for Reinforcement Learning

Lecture 4: A Comprehensive Introduction to Reinforcement Learning

Lecture 5: Advanced Concepts and Strategies in Reinforcement Learning

Lecture 6: Understanding OpenAI Frameworks for Developers

Lecture 7: Practical Exploration of OpenAI Environments

Lecture 8: Theoretical and Applied Aspects of Markov Decision Processes

Lecture 9: Solving Complex Challenges in Reinforcement Learning

Chapter 5: Solving Reinforcement Learning Problems with OpenAI/ChatGPT

Lecture 1: Strategies for Addressing Reinforcement Learning Problems with OpenAI/GPT – 1

Lecture 2: Strategies for Addressing Reinforcement Learning Problems with OpenAI/GPT – 2

Lecture 3: Strategies for Addressing Reinforcement Learning Problems with OpenAI/GPT – 3

Lecture 4: Strategies for Addressing Reinforcement Learning Problems with OpenAI/GPT – 4

Chapter 6: Deep Reinforcement Learning Applications

Lecture 1: An Introduction to Deep Reinforcement Learning

Lecture 2: Case Studies in Solving Deep Reinforcement Learning Problems

Chapter 7: Thank You

Lecture 1: Thank You!

Instructors

Rating Distribution

  • 1 stars: 8 votes
  • 2 stars: 16 votes
  • 3 stars: 44 votes
  • 4 stars: 128 votes
  • 5 stars: 216 votes

Frequently Asked Questions


How long do I have access to the course materials?


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


Can I take my courses with me wherever I go?


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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