Advanced Deep Learning Architectures

Master advanced deep learning architectures and efficient training techniques using PyTorch Lightning, timm, ConvNeXt, Vision Transformers, RoPE, SwiGLU, RMSNorm, and Weights & Biases. This course equips you to design, train, and benchmark modern backbones on limited GPU hardware for real-world production use. Module 1 introduces modern backbone architectures, tracing the evolution from ResNets to ConvNeXt and Vision Transformers, covering patch embeddings, multi-head self-attention, and position encodings. Module 2 dives into training dynamics and stabilization techniques including RMSNorm, SwiGLU activations, and Rotary Position Embeddings (RoPE) for stable, scalable training. Module 3 focuses on efficient training on limited GPUs using mixed precision (FP16/BF16), gradient accumulation, efficient data pipelines, and distributed training with DDP/FSDP in Lightning. Module 4 covers experiment tracking with TensorBoard and W&B, profiling FLOPs and throughput, and a hands-on ViT vs. CNN Showdown project with fine-tuning in timm. By the end of this course, you will: - Build and fine-tune ConvNeXt and Vision Transformer backbones using PyTorch Lightning and timm - Apply RMSNorm, SwiGLU, and RoPE to stabilize and scale deep transformer training - Implement mixed precision, gradient accumulation, and DDP/FSDP for efficient multi-GPU training - Design controlled CNN vs. ViT experiments with W&B tracking and PyTorch profiling 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.

"Production Deep Learning: Inference, Quantization & Edge Deployment is designed for ML engineers and developers who want to master the full deployment lifecycle — from compressing and quantizing models to serving them at scale using vLLM, Triton, ONNX, and Llama.cpp. Module 1 covers model compression fundamentals, including pruning, distillation, and INT8/INT4 quantization using AWQ and GPTQ, with a focus on the accuracy–latency tradeoff. Module 2 dives into high-throughput serving architectures, exploring vLLM's PagedAttention, NVIDIA Triton, TensorRT, and scaling inference across GPU clusters with autoscaling patterns. Module 3 focuses on CPU and edge deployment using ONNX Runtime, GGUF, and Llama.cpp, plus multimodal inference with CLIP and LLaVA on resource-constrained devices. Module 4 is a capstone project where you'll quantize a fine-tuned LLM, build a production API with vLLM, benchmark performance, and containerize your model with Docker for cloud and edge deployment. By the end of this course, you will: - Apply INT4/INT8 quantization techniques (AWQ, GPTQ, GGUF) to compress LLMs for production - Deploy high-throughput inference servers using vLLM, Triton, and ONNX Runtime - Run optimized models on GPU, CPU, and edge devices using Llama.cpp and TensorRT - Build, benchmark, and containerize an end-to-end production-ready inference API" 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.