Apr 23
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Various Courses that covers the goes deeper into Intermediate Concepts of Data science hat cover intermediate to advanced Machine Learning concepts, like Vector Embeddings Explained: How AI, NLP, Neural Networks & RAG Find Meaning, Reinforcement Learning with Human Feedback: A Deconstruction of Large Language Model Alignment and Deep Analysis of L1 (Lasso) and L2 (Ridge) Regularization: The Geometric Core of AI Stability. 👉 What you’ll learn on the interview prep in this video: ✅ What is the vanishing gradient problem? Why is it an issue, and how was it solved by ReLU? ✅ What is batch normalization and why does it help training? ✅Regularization technique specific to neural networks. the differences between L1 and L2 regularization ✅ What is dropout In deep learning ✅What are evaluation metrics ( precision, recall,, ROC-AUC) and when would you use each? ✅ What is k-fold Cross-Validation and why it matters ✅ What is Dimensionality Reduction and PCA (Principal Component Analysis)? Why is it used? ✅ What is the difference between generative and discriminative models? ✅Complex generative model architecture and its difficulties ✅What is double descent phenomenon in modern deep learning, and how does it ✅ What is the difference between max-margin loss (SVM) and cross-entropy loss? When would each be preferred and why? ✅Why does batch normalization important for transformer architecture ✅ Explain attention mechanisms and how they led to Transformers ✅ Catastrophic forgetting 👉 ⏱️Timestamps: 0:00 - Vector Embeddings 5:48 - Reinforcement Learning with Human Feedback (RLHF) 26:26 - L1 (Lasso) and L2 (Ridge) Regularization 41:00 - Intermediate Interview prep Trees 17:39 - Calculus ad closing thoughts 🎓 Perfect for students, AI enthusiasts, and anyone curious about how machines understand human language.🌍 Animated learning from Africa to the world — Data Science Animated by Lubula. #statistics #ai #datascience #machinelearning #deeplearning #tech