Video description
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 LevelLearn 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
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Table of Contents
Introduction
Introduction to Transformer Models for NLP: Introduction
Lesson 1: Introduction to Attention and Language Models
Topics
1.1 A brief history of NLP
1.2 Paying attention with attention
1.3 Encoder-decoder architectures
1.4 How language models look at text
Lesson 2: How Transformers Use Attention to Process Text
Topics
2.1 Introduction to transformers
2.2 Scaled dot product attention
2.3 Multi-headed attention
Lesson 3: Transfer Learning
Topics
3.1 Introduction to Transfer Learning
3.2 Introduction to PyTorch
3.3 Fine-tuning transformers with PyTorch
Lesson 4: Natural Language Understanding with BERT
Topics
4.1 Introduction to BERT
4.2 Wordpiece tokenization
4.3 The many embeddings of BERT
Lesson 5: Pre-training and Fine-tuning BERT
Topics
5.1 The Masked Language Modeling Task
5.2 The Next Sentence Prediction Task
5.3 Fine-tuning BERT to solve NLP tasks
Lesson 6: Hands-on BERT
Topics
6.1 Flavors of BERT
6.2 BERT for sequence classification
6.3 BERT for token classification
6.4 BERT for question/answering
Lesson 7: Natural Language Generation with GPT
Topics
7.1 Introduction to the GPT family
7.2 Masked multi-headed attention
7.3 Pre-training GPT
7.4 Few-shot learning
Lesson 8: Hands-on GPT
Topics
8.1 GPT for style completion
8.2 GPT for code dictation
Lesson 9: Further Applications of BERT + GPT
Topics
9.1 Siamese BERT-networks for semantic searching
9.2 Teaching GPT multiple tasks at once with prompt engineering
Lesson 10: T5 – Back to Basics
Topics
10.1 Encoders and decoders welcome: T5’s architecture
10.2 Cross-attention
Lesson 11: Hands-on T5
Topics
11.1 Off the shelf results with T5
11.2 Using T5 for abstractive summarization
Lesson 12: The Vision Transformer
Topics
12.1 Introduction to the Vision Transformer (ViT)
12.2 Fine-tuning an image captioning system
Lesson 13: Deploying Transformer Models
Topics
13.1 Introduction to MLOps
13.2 Sharing our models on HuggingFace
13.3 Deploying a fine-tuned BERT model using FastAPI
Summary
Introduction to Transformer Models for NLP: Summary