Part VI: Scaling and Efficiency

Training neural networks at scale

Overview

Part VI covers the practical challenges of training neural networks at massive scale. These papers show how to handle large datasets, massive models, and efficient distributed training—essential knowledge for modern deep learning.

Chapters

#	Chapter	Key Concept
24	Deep Speech 2	End-to-end speech at scale
25	Scaling Laws for Neural Language Models	How performance scales
26	GPipe - Pipeline Parallelism	Training giant models

The Scaling Journey

Deep Speech 2 (2015)  →  End-to-end speech, multi-GPU
       ↓
Scaling Laws (2020)   →  Understanding how to scale
       ↓
GPipe (2018)          →  Pipeline parallelism
       ↓
Modern LLMs           →  GPT-3, GPT-4, and beyond

Key Takeaway

Scaling neural networks requires understanding compute, data, and model size trade-offs, along with efficient distributed training strategies to handle models that don’t fit on a single GPU.

Prerequisites

Parts I-V (helpful for understanding design principles)
Basic understanding of distributed systems
Interest in large-scale training

What You’ll Be Able To Do After Part VI

Understand scaling laws and resource allocation
Design distributed training strategies
Appreciate the engineering behind large models
See how theory meets practice at scale