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Transformers for Natural Language Processing and Computer Vision

Master Transformers for NLP and CV, from architecture to Generative AI with GPTs, ViT, and Stable Diffusion. Build, fine-tune, and deploy.

(NLP-CV.AJ1) / ISBN : 979-8-90059-025-7
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About This Course

This course isn't about theoretical perfection; it's about getting your hands dirty with Transformers for NLP and CV. We'll dissect core Transformer Models, from their 'Attention Is All You Need' origins to advanced Generative AI applications like GPT-4 and Stable Diffusion. You'll learn to fine-tune BERT, pretrain RoBERTa, and leverage Vision Transformer (ViT) architectures. We'll tackle real-world challenges, like mitigating LLM risks and understanding tokenization's impact, because blindly deploying these models often leads to unexpected failures. Expect to build, debug, and truly understand the trade-offs involved in scaling these powerful systems.

Skills You’ll Get

  • Transformer Architecture Mastery: Deeply understand the 'Attention Is All You Need' paradigm, encoder-decoder structures, and how to implement foundational Transformer Models like BERT and RoBERTa from scratch, including their pretraining and fine-tuning nuances.

  • Generative AI Development: Gain practical expertise in leveraging and fine-tuning cutting-edge Generative AI models such as OpenAI GPTs (GPT-4, RAG), T5 for summarization, and exploring advanced LLMs like PaLM 2, understanding their capabilities and inherent limitations.

  • Computer Vision with Transformers: Develop proficiency in applying Vision Transformer (ViT) models, CLIP, and DALL-E for multimodal tasks, and master text-to-image generation with Stable Diffusion, including automated prompt design and training vision models without coding via Hugging Face AutoTrain.

  • Advanced Deployment & Risk Mitigation: Learn to interpret transformer behavior using tools like BertViz and SHAP, implement LLM embeddings as an alternative to fine-tuning, and critically assess and mitigate risks associated with large language models, paving the way for functional AGI.

Download Course Outline

1

Preface

  • Who this course is for
  • What this course covers
2

What are Transformers?

  • Foundation Models
  • A brief history of how transformers were born
  • The new role of AI professionals
  • The rise of seamless transformer APIs
  • Summary
  • References
3

Getting Started with the Architecture of the Transformer Model

  • The rise of the Transformer: Attention Is All You Need
  • Training and performance
  • Hugging Face transformer models
  • Summary
  • References
4

Emergent vs Downstream Tasks: The Unseen Depths of Transformers

  • The paradigm shift: What is an NLP task?
  • Investigating the potential of downstream tasks
  • Running downstream tasks
  • Summary
  • References
5

Advancements in Translations with Google Trax, Google Translate, and Gemini

  • Defining machine translation
  • Evaluating machine translations
  • Translations with Google Trax
  • Translation with Google Translate
  • Translation with Gemini
  • Summary
  • References
6

Diving into Fine-Tuning through BERT

  • The architecture of BERT
  • Fine-tuning BERT
  • Building a Python interface to interact with the model
  • Summary
  • References
7

Pretraining a Transformer from Scratch through RoBERTa

  • Training a tokenizer and pretraining a transformer
  • Building KantaiBERT from scratch
  • Pretraining a Generative AI customer support model on X data
  • Next steps
  • Summary
  • References
8

The Generative AI Revolution with ChatGPT

  • GPTs as GPTs
  • The architecture of OpenAI GPT transformer models
  • OpenAI models as assistants
  • Getting started with the GPT-4 API
  • Retrieval Augmented Generation (RAG) with GPT-4
  • Summary
  • References
9

Fine-Tuning OpenAI GPT Models

  • Risk management
  • Fine-tuning a GPT model for completion (generative)
  • Preparing the dataset
  • Fine-tuning an original model
  • Running the fine-tuned GPT model
  • Managing fine-tuned jobs and models
  • Before leaving
  • Summary
  • References
10

Shattering the Black Box with Interpretable Tools

  • Transformer visualization with BertViz
  • Interpreting Hugging Face transformers with SHAP
  • Transformer visualization via dictionary learning
  • Other interpretable AI tools
  • Summary
  • References
11

Investigating the Role of Tokenizers in Shaping Transformer Models

  • Matching datasets and tokenizers
  • Exploring sentence and WordPiece tokenizers to u...fficiency of subword tokenizers for transformers
  • Summary
  • References
12

Leveraging LLM Embeddings as an Alternative to Fine-Tuning

  • LLM embeddings as an alternative to fine-tuning
  • Fundamentals of text embedding with NLTK and Gensim
  • Implementing question-answering systems with embedding-based search techniques
  • Transfer learning with Ada embeddings
  • Summary
  • References
13

Toward Syntax-Free Semantic Role Labeling with ChatGPT and GPT-4

  • Getting started with cutting-edge SRL
  • Entering the syntax-free world of AI
  • Defining SRL
  • SRL experiments with ChatGPT with GPT-4
  • Questioning the scope of SRL
  • Redefining SRL
  • From task-specific SRL to emergence with ChatGPT
  • Summary
  • References
14

Summarization with T5 and ChatGPT

  • Designing a universal text-to-text model
  • The rise of text-to-text transformer models
  • A prefix instead of task-specific formats
  • The T5 model
  • Text summarization with T5
  • From text-to-text to new word predictions with OpenAI ChatGPT
  • Summary
  • References
15

Exploring Cutting-Edge LLMs with Vertex AI and PaLM 2

  • Architecture
  • Assistants
  • Vertex AI PaLM 2 API
  • Fine-tuning
  • Summary
  • References
16

Guarding the Giants: Mitigating Risks in Large Language Models

  • The emergence of functional AGI
  • Cutting-edge platform installation limitations
  • Auto-BIG-bench
  • WandB
  • When will AI agents replicate?
  • Risk management
  • Risk mitigation tools with RLHF and RAG
  • Summary
  • References
17

Beyond Text: Vision Transformers in the Dawn of Revolutionary AI

  • From task-agnostic models to multimodal vision transformers
  • ViT – Vision Transformer
  • CLIP
  • DALL-E 2 and DALL-E 3
  • GPT-4V, DALL-E 3, and divergent semantic association
  • Summary
  • References
18

Transcending the Image-Text Boundary with Stable Diffusion

  • Transcending image generation boundaries
  • Part I: Defining text-to-image with Stable Diffusion
  • Part II: Running text-to-image with Stable Diffusion
  • Part III: Video
  • Summary
  • References
19

Hugging Face AutoTrain: Training Vision Models without Coding

  • Goal and scope of this lesson
  • Getting started
  • Uploading the dataset
  • Training models with AutoTrain
  • Deploying a model
  • Running our models for inference
  • Summary
  • References
20

On the Road to Functional AGI with HuggingGPT and its Peers

  • Defining F-AGI
  • Installing and importing
  • Validation set
  • HuggingGPT
  • CustomGPT
  • Model Chaining with Runway Gen-2
  • Summary
  • References
21

Beyond Human-Designed Prompts with Generative Ideation

  • Part I: Defining generative ideation
  • Part II: Automating prompt design for generative image design
  • Part III: Automated generative ideation with Stable Diffusion
  • The future is yours!
  • Summary
  • References

1

What are Transformers?

  • Training, Evaluating, and Visualizing a Machine Learning Classifier
2

Getting Started with the Architecture of the Transformer Model

  • Implementing Multi-Head Attention and Post-Layer Normalization
  • Exploring Positional Encoding in Transformer Models
3

Emergent vs Downstream Tasks: The Unseen Depths of Transformers

  • Visualizing Decision Boundaries with k-NN Using 1000 Random Samples
  • Running Downstream Transformer Tasks
4

Advancements in Translations with Google Trax, Google Translate, and Gemini

  • Preprocessing the WMT14 French-English Dataset and Evaluating with BLEU
5

Diving into Fine-Tuning through BERT

  • Fine-Tuning BERT for Sentence Classification Using the CoLA Dataset
6

Pretraining a Transformer from Scratch through RoBERTa

  • Building and Training KantaiBERT for Token Classification
  • Building a Customer-Support Assistant Using a Transformer Model
7

The Generative AI Revolution with ChatGPT

  • Analyzing GPT Transformer Architecture and OpenAI Model APIs
  • Getting Started with OpenAI GPT-4 for NLP Tasks
  • Implementing RAG Using GPT-4
8

Shattering the Black Box with Interpretable Tools

  • Visualizing Transformer Attention with BertViz
  • Interpreting Transformer Predictions Using SHAP
9

Investigating the Role of Tokenizers in Shaping Transformer Models

  • Exploring Tokenizers in Modern NLP Using HuggingFace
10

Leveraging LLM Embeddings as an Alternative to Fine-Tuning

  • Building Word Embeddings Using NLTK and Gensim
  • Building an Embedding-Based Question-Answering and Transfer-Learning Pipeline
11

Toward Syntax-Free Semantic Role Labeling with ChatGPT and GPT-4

  • Performing Zero-Shot SRL Using GPT-4 Via Prompting
12

Summarization with T5 and ChatGPT

  • Building and Evaluating Text Summarization Systems
13

Guarding the Giants: Mitigating Risks in Large Language Models

  • Evaluating Auto-BIG-bench Tasks
  • Evaluating and Mitigating Hallucination in RAG Systems
  • Mitigating Risks in Generative AI Systems
14

Beyond Text: Vision Transformers in the Dawn of Revolutionary AI

  • Exploring Vision-Language Models with CLIP and ViT
  • Generating and Interpreting AI-Driven Visual Content Using GPT-4V and DALL·E
15

Transcending the Image-Text Boundary with Stable Diffusion

  • Generating Images with Stable Diffusion Using Keras
16

Hugging Face AutoTrain: Training Vision Models without Coding

  • Training NLP Models Automatically with Hugging Face AutoTrain
17

On the Road to Functional AGI with HuggingGPT and its Peers

  • Analyzing Images Using ViT Models

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This course targets AI professionals, data scientists, and machine learning engineers who want to move beyond theoretical understanding to practical implementation and deployment of advanced Transformer Models in NLP and Computer Vision. It assumes a foundational understanding of Python and machine learning concepts.

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p dir="ltr">You'll build and fine-tune models for machine translation, text summarization, question-answering systems, semantic role labeling, and cutting-edge text-to-image generation. We also cover integrating with APIs like GPT-4 and Vertex AI PaLM 2 for real-world Generative AI solutions.

Absolutely. We dive into the architecture and application of current models like BERT, RoBERTa, T5, OpenAI GPTs (including GPT-4 and RAG), Vision Transformer (ViT), CLIP, DALL-E 3, Stable Diffusion, and PaLM 2, ensuring you're up-to-date with the Generative AI landscape.

We explicitly address critical aspects like the trade-offs in fine-tuning vs. embeddings, the role of tokenizers in model performance, interpreting black-box models, and significant risks associated with large language models, including ethical considerations and platform limitations. Expect to learn how to debug and mitigate common failure points.

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