Ideal for teams that…
Hands-on AI and data analytics workshops — built around your team's real cases.
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
What we actually do
- · 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)
- · 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
- · 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)
- · 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
- · 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
- · 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
- · 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
From brief to retro in 30 days.
Brief & diagnosis
A call with the team lead + a short survey for participants. We define goals, gap and context.
Program customization
We adapt modules, case studies and code examples to your stack. Approval in 5 days.
Workshop
Trainer-led sessions, hands-on, code review. Mentor available between sessions too.
Retro + report
Outcome report for the team and lead. 30 days of consulting included.
Send a brief. We'll reply within 1 day.
After a short brief we'll prepare a program and a quote. No obligations — it's just a starting point.
Thank you!
We'll get back to you within 1 business day.
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