Advanced Deep Learning Architectures

About this course

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.

Course objectives

• enhance understanding of modern deep learning dynamics
• develop practical skills for deploying large-scale models on limited hardware
• gain insights into Vision Transformers and ConvNeXt