Understanding AI models is the foundation.
IBM's complete playlist. 34 videos. Free.
Most engineers jump straight to LLMs without understanding the basics.
→ Can't explain how CNNs work
→ Don't know when to use transformers vs LSTMs
→ Confused about GANs and autoencoders
→ Lost when models fail in production
This playlist builds the foundation.
➕ What's covered:
➡️ Core AI Architectures
→ CNNs, GANs, Transformers, LSTMs, Autoencoders
→ How each architecture works
→ When to use which model
➡️ Language Models
→ How Large Language Models Work
→ NLP vs NLU vs NLG
→ Zero Shot Reasoning
→ Building RAG systems
➡️ Training & Optimization
→ Backpropagation explained
→ Gradient Descent
→ Overfitting and Underfitting
→ Federated Learning
➡️ ML Fundamentals
→ Supervised vs Unsupervised Learning
→ Random Forest
→ Time Series Analysis
→ Monte Carlo Simulation
➡️ Production AI
→ MLOps
→ PyTorch for training and inference
→ Embedded AI
→ Edge AI vs Distributed AI
➡️ Modern AI
→ AI Agents
→ Large Reasoning Models (LRMs)
→ LLM as a Judge
→ AI Biases and Trust
➕ Real outcomes:
✓ Understand different AI architectures
✓ Know which model to use for your problem
✓ Debug training issues
✓ Deploy models to production
✓ Evaluate AI systems properly
From IBM Technology:
Perfect for:
→ AI engineers building foundations
→ ML engineers understanding architectures
→ Developers learning AI models
→ Anyone deploying AI systems
Build the foundation first. Everything else follows.
(Playlist in comments)
♻️ Repost to save someone $$$ and a lot of confusion.
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