Connie ZhouApplied Machine Learning — Part 25 Understanding DNN Backpropagation: A Powerful Tool in Machine…IntroductionJul 1
James Koh, PhDinMITB For AllSingular Value Decomposition — Explained step by step with codeWhat it is intuitively, with numbers to illustrateFeb 61
Connie ZhouApplied Machine Learning — Part 24 A Practical Guide: Loss and Gradient in Deep Neural NetworksDeep neural networks (DNNs) have revolutionized many fields, from computer vision to natural language processing. Central to the training…Jun 24Jun 24
Connie ZhouApplied Machine Learning — Part 12 : Principal Coordinate Analysis (PCoA) in PythonIn the vast landscape of data analysis, uncovering hidden patterns and relationships is often the key to unlocking valuable insights…Apr 1Apr 1
Connie ZhouApplied Machine Learning — Part 23 Why you need Gaussian Mixture Models (GMM) instead of K-meansWhy Gaussian Mixture Models Matter in Machine LearningJun 17Jun 17
Connie ZhouApplied Machine Learning — Part 25 Understanding DNN Backpropagation: A Powerful Tool in Machine…IntroductionJul 1
James Koh, PhDinMITB For AllSingular Value Decomposition — Explained step by step with codeWhat it is intuitively, with numbers to illustrateFeb 61
Connie ZhouApplied Machine Learning — Part 24 A Practical Guide: Loss and Gradient in Deep Neural NetworksDeep neural networks (DNNs) have revolutionized many fields, from computer vision to natural language processing. Central to the training…Jun 24
Connie ZhouApplied Machine Learning — Part 12 : Principal Coordinate Analysis (PCoA) in PythonIn the vast landscape of data analysis, uncovering hidden patterns and relationships is often the key to unlocking valuable insights…Apr 1
Connie ZhouApplied Machine Learning — Part 23 Why you need Gaussian Mixture Models (GMM) instead of K-meansWhy Gaussian Mixture Models Matter in Machine LearningJun 17
Connie ZhouApplied Machine Learning — Part 13: Understanding Canonical Correlation Analysis(CCA): A Practical…In the realm of machine learning, understanding relationships between variables is crucial for building robust models. Canonical…Apr 8
Connie ZhouApplied Machine Learning — Part 22: Why you need Discrete Markov Random Fields (MRFs)Discrete Markov Random Fields (MRFs) are powerful probabilistic models used for representing spatial or contextual dependencies in data…Jun 10
Connie ZhouApplied Machine Learning — Part 2: Classification Naive Bayes from Math to Python ImplementationClassification is a fundamental task in machine learning, and one powerful algorithm for this purpose is Naive Bayes. This year, I’m…Jan 22