Training: Transformer Models with PyTorch
Level
AdvancedDuration
24h / 3 daysDate
Individually arrangedPrice
Individually arrangedTraining: Transformer Models with PyTorch
The Transformer Models with PyTorch training is an intensive 2–3 day workshop designed to teach participants how to build and deploy modern Transformer models from scratch using the PyTorch library. The program combines theoretical knowledge (20%) with a strong focus on practice (80%), covering key components such as the self-attention mechanism, multi-head attention, encoder and decoder layers, and the complete Transformer architecture with examples of training and model evaluation. The course prepares participants to independently implement and optimize Transformer models used in NLP and beyond.
What will you learn?
- Gain a detailed understanding of Transformer architecture and its implementation in PyTorch
- Understand attention mechanisms and their practical use in sequential models
- Learn to build complete Transformer models from scratch for various tasks
- Master masking techniques, metric selection, and result interpretation
- Acquire skills in training, validation, fine-tuning, and visualization of Transformer behavior
- Prepare a production-ready model with simple API integration
Who is this training for?
Developers and machine learning engineers who want to master Transformer architecture-
NLP specialists aiming to understand the inner workings and applications of Transformer models
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Data scientists and researchers working with sequential and contextual data
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Professionals interested in hands-on deep learning coding with PyTorch
Training Program
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Day 1: Fundamentals of Transformer Architecture and Core Component Implementation
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Module 1: Introduction to Transformer Architecture
- Origins of Transformers: what they are and why they revolutionized NLP and AI
- Architecture overview: encoder, decoder, and encoder-decoder setups
- Self-attention mechanism – theory and intuition
- Roles and functions of key layers: multi-head attention, feed-forward networks, positional encoding
- Transformer use cases in sequential tasks (user behavior sequence analysis, product recommendations, financial time series analysis)
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Module 2: Implementing Core Components in PyTorch
- Coding Multi-Head Attention from scratch in PyTorch
- Defining Position-Wise Feed-Forward Networks
- Implementing Positional Encoding (including RoPE alternatives)
- Lab: building individual components and testing them on synthetic data
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Day 2: Building and Training a Full Transformer Model
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Module 3: Constructing an Encoder-Decoder Model
- Combining encoder and decoder layers into a full Transformer
- Architectures: encoder-only, decoder-only, encoder-decoder
- Adapting models for NLP and other sequential tasks (text classification, generation, translation, recommendation, user sequence analysis)
- Forward pass, token masking (padding, look-ahead masks)
- Practical lab: building a complete Transformer model in PyTorch (translation or sequence generation example)
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Module 4: Training, Validation, and Evaluation
- Defining loss functions and optimizers for Transformer models (CrossEntropy, label smoothing)
- Training and validation loops with metric monitoring
- Visualizing self-attention – interpreting model behavior
- History masking for sequence generation (look-ahead masks)
- Choosing and implementing metrics (accuracy, perplexity, BLEU, F1, etc.)
- Practical deployment: saving models, inference, building a simple API (FastAPI/Flask)
- Workshop: training and evaluating the model on real-world datasets
- Hugging Face comparison: using pre-trained models, fine-tuning, and use cases
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Day 3: Advanced Techniques and Optimizations
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Module 5: Optimizations and Extensions of Transformers
- Preventing overfitting: dropout, layer normalization, residual connections
- Using pre-trained models, transfer learning, and fine-tuning with PyTorch Transformers
- Model scaling: parameter adjustments, batch size, mixed precision training
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Module 6: LoRA, Scaling, and Advanced Fine-Tuning
- Introduction to Low-Rank Adaptation (LoRA) in Transformers
- Efficient fine-tuning strategies for large models
- Scaling models and managing GPU memory
- Practical lab: fine-tuning a model on custom data and business-specific tasks
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Module 7: Deployment and Integration of Transformer Models
- Preparing a Transformer model for production use
- Creating a simple API to expose the model using Flask/FastAPI
- Overview of PyTorch tools for saving and loading models
- Final workshop: deploying and testing the model in a local or cloud environment
