Introduction to Transformer Models for NLP: Using BERT, GPT, and More to Solve Modern Natural Language Processing Tasks
Год выпуска: August 2022
Производитель: Pearson via O'Reilly Learning
Сайт производителя:
https://learning.oreilly.com/course/introduction-to-transformer/9780137923717/
Автор: Sinan Ozdemir
Продолжительность: 10h 14m
Тип раздаваемого материала: Видеоурок
Язык: Английский + субтитры
Описание:
10+ Hours of Video Instruction
Learn how to apply state-of-the-art transformer-based models including BERT and GPT to solve modern NLP tasks.
Overview
Introduction to Transformer Models for NLP LiveLessons provides a comprehensive overview of transformers and the mechanisms—attention, embedding, and tokenization—that set the stage for state-of-the-art NLP models like BERT and GPT to flourish. The focus for these lessons is providing a practical, comprehensive, and functional understanding of transformer architectures and how they are used to create modern NLP pipelines. Throughout this series, instructor Sinan Ozdemir will bring theory to life through illustrations, solved mathematical examples, and straightforward Python examples within Jupyter notebooks.
All lessons in the course are grounded by real-life case studies and hands-on code examples. After completing this lesson, you will be in a great position to understand and build cutting-edge NLP pipelines using transformers. You will also be provided with extensive resources and curriculum detail which can all be found at the course’s GitHub repository.
About the Instructor
Sinan Ozdemir’is currently Founder and CTO of Shiba Technologies. Sinan is a former lecturer of Data Science at Johns Hopkins University and the author of multiple textbooks on data science and machine learning. Additionally, he is the founder of the recently acquired Kylie.ai, an enterprise-grade conversational AI platform with RPA capabilities. He holds a master’s degree in Pure Mathematics from Johns Hopkins University and is based in San Francisco, CA.
Skill Level
• Intermediate
• Advanced
Learn How To
• Recognize which type of transformer-based model is best for a given task
• Understand how transformers process text and make predictions
• Fine-tune a transformer-based model
• Create pipelines using fine-tuned models
• Deploy fine-tuned models and use them in production
Who Should Take This Course
• Intermediate/advanced machine learning engineers with experience with ML, neural networks, and NLP
• Those interested in state-of-the art NLP architecture
• Those interested in productionizing NLP models
• Those comfortable using libraries like Tensorflow or PyTorch
• Those comfortable with linear algebra and vector/matrix operations
Course Requirements
• Python 3 proficiency with some experience working in interactive Python environments including Notebooks (Jupyter/Google Colab/Kaggle Kernels)
• Comfortable using the Pandas library and either Tensorflow or PyTorch
• Understanding of ML/deep learning fundamentals including train/test splits, loss/cost functions, and gradient descent
Содержание
Introduction
Lesson 01 Introduction to Attention and Language Models
Lesson 02 How Transformers Use Attention to Process Text
Lesson 03 Transfer Learning
Lesson 04 Natural Language Understanding with BERT
Lesson 05 Pre-training and Fine-tuning BERT
Lesson 06 Hands-on BERT
Lesson 07 Natural Language Generation with GPT
Lesson 08 Hands-on GPT
Lesson 09 Further Applications of BERT + GPT
Lesson 10 T5 - Back to Basics
Lesson 11 Hands-on T5
Lesson 12 The Vision Transformer
Lesson 13 Deploying Transformer Models
Summary
Файлы примеров: отсутствуют
Формат видео: MP4
Видео: AVC, 1280×720, 16:9, 30.000 fps, 3 000 kb/s (0.017 bit/pixel)
Аудио: AAC, 44.1 KHz, 2 channels, 128 kb/s, CBR
Доп. информация:
Ancillary files for this LiveLesson can be accessed at
https://github.com/sinanuozdemir/oreilly-transformers-video-series.
