Machine Learning & StatisticsUnderstanding the Universal Approximation TheoremIntroductionDec 25, 20231Dec 25, 20231
Machine Learning & StatisticsStochastic Gradient Descent: Unveiling the Core of Neural Network TrainingIntroductionNov 29, 2023Nov 29, 2023
Machine Learning & StatisticsReducing Noise and Improving Interpretability in CNNs: A Technical Review of the SmoothGrad Method…In a previous blog post (Understanding the predictions of convolutional neural networks: saliency maps), we discussed the importance of…Feb 14, 2023Feb 14, 2023
Machine Learning & StatisticsUnderstanding the predictions of convolutional neural networks: saliency mapsAs data scientists, we often work with deep learning models to solve a wide range of problems. One of the challenges of working with deep…Feb 14, 2023Feb 14, 2023
Machine Learning & StatisticsLIME: Local Interpretable Model-Agnostic Explanations (Part 4)In the previous parts of this series, we introduced LIME (Local Interpretable Model-Agnostic Explanations) and discussed the methodology…Feb 12, 2023Feb 12, 2023
Machine Learning & StatisticsLIME: Local Interpretable Model-Agnostic Explanations — part 3We introduced LIME (Local Interpretable Model-Agnostic Explanations) and discussed the LIME methodology in Parts 1 and 2 of this series…Feb 10, 2023Feb 10, 2023
Machine Learning & StatisticsLIME: Local Interpretable Model-Agnostic Explanations (Part 2)In Part 1 of this series, we introduced LIME (Local Interpretable Model-Agnostic Explanations) and discussed its importance in the field of…Feb 9, 2023Feb 9, 2023
Machine Learning & StatisticsLIME: Local Interpretable Model-Agnostic Explanations — part 1As a data scientist, it is crucial to understand the workings of machine learning models, not only to build better models, but also to…Feb 8, 2023Feb 8, 2023