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Transformers for Natural Language Processing and Computer Vision

Bridge the gap between language and sight! Master the state-of-the-art Transformer Models that are currently redefining the boundaries of AI.

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

Are you ready to stop treating text and images as separate worlds? The "Transformer Revolution" has unified the way we build intelligence. This course is your deep-dive into the most powerful architecture in AI history—the Transformer.

We move beyond the basics of BERT and GPT-4 to show you how the same fundamental principles power both human-like text generation and superhuman computer vision. You’ll explore the Transformer ecosystem, mastering everything from the initial Encoder-Decoder stack to advanced Vision Transformer (ViT) implementations.

Whether you’re building a multi-modal assistant, fine-tuning an LLM using RAG (Retrieval-Augmented Generation), or generating photorealistic art with Stable Diffusion, this course provides the blueprint. We don't just talk about theory; we dive into 30+ virtual labs where you’ll perform Fine-tuning Transformers for real-world production. This is your chance to master Generative AI across both pixels and prose.

Skills You’ll Get

  • Architectural Mastery: Understand the core mechanics of Transformer Models, including positional encoding, multi-head attention, and the evolution from CNNs/RNNs to the modern Encoder-Decoder stack.
  • NLP & Semantic Analysis: Master BERT and GPT-4 for tasks like SRL (Semantic Role Labeling), text summarization with T5, and complex NLU benchmarks like GLUE and SuperGLUE.
  • Vision & Multimodal AI: Implement the Vision Transformer (ViT), explore CLIP for image-text pairing, and master the multi-phase workflow of Stable Diffusion for creative content generation.
  • Production & Risk Management: Learn Fine-tuning Transformers for custom datasets, implement RAG to mitigate hallucinations, and establish robust guardrails for Ethical AI and risk management.
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 NLKT 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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While CNNs look at local pixel patterns, the Vision Transformer (ViT) uses self-attention to understand global relationships across the entire image. This allows for superior performance on large-scale datasets and complex visual reasoning tasks.

Absolutely. RAG is a core component of this course. You will learn how to connect BERT and GPT-4 to external data sources to reduce hallucinations and provide accurate, context-aware answers in production environments.

We make it approachable! You’ll walk through step-by-step guides for Fine-tuning Transformers on specific datasets like CoLA for NLP and custom image sets for CV, using tools like Hugging Face and OpenAI APIs.

Yes! You will get hands-on experience with Stable Diffusion and DALL-E. You’ll learn how to automate generative-AI image prompt design and manage the different phases of diffusion models to create high-quality visual content.

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