This three-course specialization is built for engineers who have moved past the basics and are ready to tackle the complexities of modern, massive deep learning architectures. You will go under the hood of Transformers and Diffusion Models — mastering not just how they work, but how to fine-tune and optimize them for specific use cases without needing a million-dollar compute cluster. Starting with advanced architectures, you will work with Vision Transformers, ConvNeXt, and modern training dynamics including RMSNorm, SwiGLU activations, and Mixed Precision Training using PyTorch Lightning and Timm.

As you progress, you will deep-dive into decoder-only Transformer internals, KV Caching, and Parameter-Efficient Fine-Tuning using LoRA and QLoRA to fine-tune billion-parameter models on consumer GPUs.

Disclaimer: This is an independent educational resource created by Board Infinity for informational and educational purposes only. This course is not affiliated with, endorsed by, sponsored by, or officially associated with any company, organization, or certification body unless explicitly stated. The content provided is based on industry knowledge and best practices but does not constitute official training material for any specific employer or certification program. All company names, trademarks, service marks, and logos referenced are the property of their respective owners and are used solely for educational identification and comparison purposes.

应用的学习项目

Throughout the specialization, learners complete applied engineering projects that progressively build toward production-ready deep learning systems. You will fine-tune ConvNeXt and Vision Transformer models on fine-grained classification datasets using Mixed Precision Training, comparing convergence speed and accuracy across architectures with TensorBoard and Weights & Biases tracking. learners use QLoRA to fine-tune a large language model such as Llama-3-8B on domain-specific data, producing a lightweight adapter that transforms model behavior entirely. The final project brings everything together — learners quantize their fine-tuned model to 4-bit GGUF format, benchmark perplexity loss against speed gains, and containerize the model using vLLM to create an API endpoint capable of handling concurrent requests.