• About
• Objectives
• Syllabus
• Certification
• Career & Jobs
• Interviews
• Quiz
• FAQs

Transformers have revolutionised the landscape of artificial intelligence, enabling breakthroughs in natural language processing (NLP), computer vision, speech recognition, and generative AI. From BERT and GPT to multimodal models like CLIP, Flamingo, and LLaVA, transformer architectures now form the foundation of modern AI systems used across industries. Their ability to capture long-range dependencies, train efficiently on massive datasets, and generalise across diverse tasks has made them the most influential innovation in deep learning over the last decade.

The Transformers course by Uplatz offers a deeply practical and comprehensive journey into understanding, building, fine-tuning, and deploying transformer-based AI systems. You will explore every critical component — from self-attention to positional encoding, encoder–decoder architectures, masked language modeling, sequence generation, and multimodal fusion. By mastering these concepts, learners gain the knowledge required to develop real-world AI applications, optimise transformer models, and integrate them into production environments.

This course starts with foundational concepts, explaining how transformers emerged as an alternative to recurrent and convolutional models. You will learn how self-attention replaced sequential computation, enabling parallel processing and more scalable training. The course breaks down the mathematics behind dot-product attention, query/key/value projections, feed-forward networks, and residual connections — ensuring you understand not only what transformers do but how they operate internally.

The heart of the course explores transformer families and architectures, including:

• Encoder-only models (BERT, RoBERTa, DistilBERT)

• Decoder-only models (GPT, GPT-2/3/4)

• Encoder–decoder models (T5, BART)

• Vision transformers (ViT, DeiT, Swin)

• Speech and audio transformers

• Multimodal models (CLIP, LLaVA, Flamingo)

Each model family is covered with clear explanations of training objectives, masking strategies, tokenisation workflows, and downstream tasks.

Hands-on implementation is a central part of this course. You will learn to load, fine-tune, evaluate, and deploy transformers using:

• Hugging Face Transformers

• PyTorch

• TensorFlow

• PEFT (LoRA, QLoRA, adapters)

• Tokenizers library

• Model quantisation and optimisation tools

The course includes guided labs for:

• Text classification

• Named entity recognition

• Question answering

• Summarisation

• Translation

• Text generation

• Chatbot and dialogue modelling

• Vision transformer classification

• Multimodal retrieval tasks

Beyond model training, the course explores efficient adaptation techniques, such as:

• LoRA and QLoRA

• Prefix tuning

• Adapter layers

• 8-bit and 4-bit quantisation

• Distillation for resource-constrained environments

You will understand how these techniques help teams train transformer models on low-cost hardware and deploy them efficiently in production.

The course also covers transformers in generative AI, where you will learn about:

• Autoregressive generation

• Beam search, sampling, temperature scaling

• Tokenisation strategies and vocabulary optimisation

• Reinforcement learning from human feedback (RLHF)

• Safety alignment

• Prompt engineering and instruction tuning

You will study how transformer-based large language models (LLMs) generate coherent text, respond to instructions, and perform tasks across domains.

Transformers are no longer limited to text. The course includes modules on their applications in:

• Computer vision (Vision Transformers, DeiT, Swin)

• Speech recognition and audio modelling

• Multimodal fusion for images + text

• Embedding generation for retrieval systems

• Video transformers

By incorporating real-world datasets and practical examples, the course ensures learners can adapt transformer architectures to diverse business needs.

An essential section of the course explores how transformers are deployed in production. You will learn:

• Model serving with FastAPI, TorchServe, and Hugging Face Inference

• Scaling models with GPUs, distributed training, and serverless inference

• Caching, batching, and API optimisation

• Monitoring model performance

• Managing model drift and updates

You will also explore transformer security considerations such as prompt injection, jailbreak risks, content safety, and bias mitigation.

Finally, the course includes industry case studies that show how transformers power real AI systems:

• Search and recommendation engines

• Document intelligence and OCR

• Customer support automation

• Healthcare NLP

• Image-text retrieval

• Fraud detection

• Conversational AI agents

By the end of the course, learners will have a deep, practical mastery of transformer architectures and will be prepared to build cutting-edge AI applications.