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Transformer Models and Applications

Course Description

Transformer Models and Applications – Mastering Modern AI Architectures is an 8-week intensive program designed to make you proficient in the most powerful AI architectures powering today’s language, vision, and multi-modal applications. From the basics of self-attention to advanced use-cases like large language models and deployment strategies, you will get hands-on experience through multiple projects and a final capstone that builds your real-world AI portfolio.

 

What Will You Learn?

  • Foundations of Transformer Architecture and why it outperforms RNNs and CNNs.

  • How to use Hugging Face Transformers for quick model implementation and fine-tuning.

  • Real-world NLP Applications like sentiment analysis, translation, and question answering.

  • Implement Vision Transformers (ViT) for image classification and detection tasks.

  • Explore Multimodal Transformers like CLIP, Gemini, and audio transformers like Whisper.

  • Prompt Engineering techniques for Large Language Models (LLMs).

  • Skills to Deploy Transformers on cloud platforms using ONNX and TorchServe.

  • Tips for Optimizing models with quantization and pruning for production-ready systems.

  • Complete a Capstone Project to build and deploy a full AI solution with Transformer models.

Course Curriculum

  • Introduction to Transformer Models
    • What are Transformers?
    • Evolution from RNNs and CNNs to Transformers
    • Why Transformers Revolutionized Deep Learning
    • Key Concepts: Self-Attention, Positional Encoding, Multi-head Attention

  • Deep Dive into Transformer Architecture
    • Encoder-Decoder Structure
    • Layer Normalization, Residual Connections
    • BERT, GPT, T5, RoBERTa – Architectural Variations
    • Vision Transformers (ViT) vs Language Transformers

  • Working with Hugging Face Transformers
    • Introduction to Hugging Face Ecosystem
    • Tokenizers, Pipelines, and Pretrained Models
    • Fine-Tuning Transformers on Custom Datasets
    • Transformers with PyTorch & TensorFlow

  • Natural Language Processing Applications
    • Text Classification, Sentiment Analysis
    • Named Entity Recognition
    • Question Answering Systems
    • Machine Translation

  • Vision Applications of Transformers
    • Image Classification using ViT
    • Object Detection with DETR
    • Comparison with CNN-Based Models
    • Building End-to-End Vision Pipelines

  • Advanced Applications & Multi-Modal Transformers
    • Prompt Engineering for LLMs
    • Transformers in Audio & Speech (Whisper)
    • Cross-modal Transformers: CLIP, Flamingo, Gemini
    • Intro to Large Language Models (LLMs)

  • Deployment & Optimization
    • Model Quantization & Pruning
    • Deploying on AWS/GCP using ONNX & TorchServe
    • Building Scalable Transformer APIs
    • Cost-Effective Inference Tips

  • pstone Project & Certification
    • Pick a domain: NLP / Vision / Multimodal
    • Real-World Use Case: Build & Deploy a Transformer-Based Solution
    • GitHub Portfolio Submission
    • Final Quiz & Project Evaluation
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Chronolearn

Developer

I am a web developer with a vast array of knowledge in many different front end and back end languages, responsive frameworks, databases, and best code practices

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This Course Fee:

₹29,999.00

₹40,000.00
Course includes:
  • img Level
      Beginner Intermediate Expert
  • img Duration 40h
  • img Passing Marks 80
  • img Exam Duration 1h 30m
  • img Certifications Yes
  • img Language
      English German Arabic French Spanish
  • img Access 90 Days
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