Sandeep97 articles in Deep Learning — page 1 of 5. Practical, up-to-date guides written to be found, answered, and cited.
Is Model Distillation Pipelines Ready for Prime Time? An Honest Assessment — a practical 2026 guide to model distillation pipelines ready, updated for 2026.
Getting Started With Model Distillation Pipelines: A Developer Walkthrough — a practical 2026 guide to getting started, for developers and founders.
The Developer's Roadmap to Model Distillation Pipelines — a practical 2026 guide to developer's roadmap to model distillation, for developers and founders.
Model Distillation Pipelines in Production: Lessons and Pitfalls — a practical 2026 guide to model distillation pipelines, for developers and founders.
How Quantization With AWQ and GPTQ Works Under the Hood — a practical 2026 guide to quantization, core concepts, best practices, real data and FAQs.
Quantization With AWQ and GPTQ: Mistakes Teams Make and How to Avoid Them — a practical 2026 guide to quantization, for developers and founders.
Is Quantization With AWQ and GPTQ Ready for Prime Time? An Honest Assessment — a practical 2026 guide to quantization, for developers and founders.
Getting Started With Quantization With AWQ and GPTQ: A Developer Walkthrough — a practical 2026 guide to getting started, for developers and founders.
LoRA Fine-Tuning: A Practical Guide for 2027 — a practical 2026 guide to lora fine tuning: a practical guide, for developers and founders, updated for 2026.
LoRA Fine-Tuning: Interview Questions to Expect in 2027 — a practical 2026 guide to lora fine tuning: interview questions, for developers and founders.
Why Mixture-of-Experts Won: The Architecture Behind Modern LLMs — a practical 2026 guide to mixture of experts won: the architecture behind, updated for 2026.
How LoRA Fine-Tuning Works Under the Hood — a practical 2026 guide to under the hood, core concepts, best practices, real data and FAQs, updated for 2026.
LoRA Fine-Tuning: Mistakes Teams Make and How to Avoid Them — a practical 2026 guide to lora fine tuning: mistakes teams, for developers and founders.
The Developer's Roadmap to State-Space Models Like Mamba — a practical 2026 guide to developer's roadmap to state space models, for developers and founders.
State-Space Models Like Mamba in Production: Lessons and Pitfalls — a practical 2026 guide to state space models like mamba, for developers and founders.
State-Space Models Like Mamba: A Practical Guide for 2027 — a practical 2026 guide to state space models like mamba:, for developers and founders.
State-Space Models Like Mamba: Interview Questions to Expect in 2027 — a practical 2026 guide to state space models like mamba: interview, updated for 2026.
Is Mixture-of-Experts Architectures Ready for Prime Time? An Honest Assessment — a practical 2026 guide to mixture of experts architectures ready.
Getting Started With Mixture-of-Experts Architectures: A Developer Walkthrough — a practical 2026 guide to getting started, for developers and founders.
The Developer's Roadmap to Mixture-of-Experts Architectures — a practical 2026 guide to developer's roadmap to mixture of experts architectures.
Mixture-of-Experts Architectures in Production: Lessons and Pitfalls — a practical 2026 guide to mixture of experts architectures, by Sandeep Kumar Chaudhary.
Why Contrastive Learning Beats Manual Labels for Image Models — a practical 2026 guide to contrastive learning beats manual labels, updated for 2026.
Active Learning Interview Questions for ML Engineers in 2026 — a practical 2026 guide to active learning interview questions, for developers and founders.
How Self-Supervised Pretraining Cuts Your Labeling Costs — a practical 2026 guide to self supervised pretraining cuts your labeling, updated for 2026.