Interview Questions Part III & IV - Training and Optimization (11-20)

🚀 Master AI Training & Optimization in Under 7 Minutes | Complete Guide to LLM Training Secrets
🧠 Transform Your AI Knowledge From Beginner to Expert!
Ever wondered how ChatGPT, GPT-4, and other breakthrough AI models are actually trained? This power-packed 7-minute masterclass reveals the 10 ESSENTIAL training techniques that separate AI practitioners from AI experts!
⚡ What You'll Master:
✅ Autoregressive Language Modeling - The secret sauce behind GPT's coherent text generation
✅ Learning Rate Scheduling - Why warmup + decay creates stable $1M+ training runs
✅ Gradient Clipping - The 1.0 magic number that prevents catastrophic training failures
✅ Mixed Precision Training - How FP32 + FP16 cuts training costs by 50%
✅ Adam Optimizer - Why β₁=0.9, β₂=0.999 dominates LLM training
✅ Weight Decay - The 0.01 value that improves generalization by 20%
✅ Batch Size Effects - How larger batches create training stability
✅ Gradient Accumulation - Simulate massive batches on limited GPU memory
✅ Loss Scaling - Prevent 40% of gradients from vanishing in FP16
✅ Data Parallelism - Scale from 1 GPU to 1000+ GPUs like GPT-3
🎯 Perfect For:

AI Engineers wanting to optimize model training
ML Researchers seeking practical implementation insights
Data Scientists transitioning to large language models
Students preparing for AI/ML interviews
Tech Enthusiasts curious about how AI really works

🔥 Why This Matters:
These aren't just theoretical concepts - they're the EXACT techniques used to train:

GPT-3 ($12M training cost, 1024 GPUs, 34 days)
ChatGPT and GPT-4
Claude, PaLM, and other state-of-the-art models

⏰ Timestamps:
0:00 - Pretraining Objectives (Autoregressive Magic)
0:40 - Learning Rate Scheduling (Warmup + Decay)
1:22 - Gradient Clipping (The 1.0 Standard)
2:00 - Mixed Precision Training (FP32 + FP16)
2:43 - Adam Optimizer (Beta Values Mastery)
3:24 - Weight Decay (0.01 Regularization)
4:03 - Batch Size Effects (Stability Secrets)
4:43 - Gradient Accumulation (Memory Optimization)
5:24 - Loss Scaling (Gradient Preservation)
6:07 - Data Parallelism (Scaling to 1000+ GPUs)
💡 Memory Techniques Included:
Every concept comes with unforgettable mnemonics:

"AUTO-regressive = AUTOmatically forward"
"Adam loves the number NINE"
"ONE size fits all models"
Plus 7 more brain-sticking memory hacks!

🎓 Level Up Your AI Career:
Master these fundamentals and you'll understand the core principles behind every major AI breakthrough. Whether you're optimizing training costs, debugging convergence issues, or scaling to production, these concepts are your foundation.

🚀 Ready to Build the Future?
Hit that LIKE button if this saved you hours of research, SUBSCRIBE for more AI deep-dives, and SHARE with anyone ready to level up their machine learning game!

🏷️ Tags: #AI #MachineLearning #DeepLearning #GPT #Transformers #LLM #NeuralNetworks #ArtificialIntelligence #TechEducation #Programming #DataScience #MLOps #AITraining #ComputerScience #TechTutorial

Remember: Every AI expert was once a beginner. Your journey to mastering the technology that's reshaping the world starts now!

Видео Interview Questions Part III & IV - Training and Optimization (11-20) канала JVL Learn's Code Easy