Terminology / Key Concepts

1 Glossary / Terminology Explained at High Level 2 AI, ML, DL, GenAI, Agents etc 3 Frameworks & Libraries in ML (Machine Learning) 4 Feature Engineering 5 TensorFlow & PyTorch (ML Frameworks) 6 TensorFlow, Tensor & SafeTensors 7 NumPy, Matplotlib, Spark MLlib, Scikit-learn 8 Keras (Neural Network API) 9 Tools:Kubeflow vs MLFlow vs AWS vs Azure vs Google 10 Ensemble Methods: Bagging, Boost, Stack, Vote.. 11 AutoML 12 LLMOps(large language model operations) 13 LangChain 14 LangGraph 15 Adversarial Machine Learning 16 n8n vs LangGraph: Detailed Comparison 17 Vector Databases (FAISS) & Embeddings 18 ChromaDB 19 Transformers in a mathematical way 20 Transformers