Deep Learning Training Playbook: Turning Research Papers into Engineering Decisions

Deep Learning is evolving faster than ever.

Every week we see new techniques:

• LoRA
• QLoRA
• RAG
• Foundation Models
• Mamba
• Diffusion Models

But one question is still difficult:

Which technique should I choose for my problem?

Most resources explain how something works.

Very few explain:

• Why was it created?
• When should I use it?
• When should I avoid it?
• What are the trade-offs?

That is the problem I wanted to solve.

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Introducing: Deep Learning Training Playbook

A practical guide to help engineers make better AI decisions.

The first part covers:

✅ Transfer Learning

✅ Foundation Models

✅ Full Fine-Tuning

✅ LoRA

✅ QLoRA

✅ Continued Pretraining

✅ Knowledge Distillation

The goal:

Don't memorize AI techniques. Learn when and why to use them.

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Open Source

GitHub:

https://github.com/wildoctopus/deep-learning-training-playbook

More chapters coming:

• Model Architectures
• Generative AI
• Training Optimization
• Agentic AI
• Production AI Systems

Built by WildOctopus 🐙

Turning research papers into practical engineering playbooks.

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