Markov chain Monte Carlo is a powerful technique that combines the concepts of Markov chain and Monte Carlo methods. It provides a way to sample from otherwise intractable probability distributions. MCMC methods are statistical techniques that provide a way to sample from complex, high-dimensional probability distributions that are otherwise intractable or difficult to analyze mathematically. MC..

Unsupervised or Supervised Classification This post will focus on classification algorithms, such as K-means, model-based clustering, K nearest neighbours and probabilistic classifiers. K-means K-means clustering is a popular unsupervised machine learning algorithm to partition a dataset into k distinct clusters. It aims to minimize the within-cluster sum of squares by assigning data points to c..

Principal Components Analysis (PCA) is a popular technique for analysing high-dimensional data. PCA is a mathematical procedure that transforms a set of correlated variables into a new set of uncorrelated variables, called "Principal Components" while retaining most of the variability of the original data. To perform PCA on a dataset of N, p-dimensional data point x_j ∈ Rᴾ, we need to follow the..

*** This post is a summary version of the below website. The website has more details and references. https://machinelearningmastery.com/batch-normalization-for-training-of-deep-neural-networks/ One possible reason for the difficulties of having changeable inputs to layers deep in the network("Internal Covariate Shift) is due to updating weights per mini-batch. Batch Normalisation can stabilise ..

https://south-crown-f53.notion.site/Lecture-Note-Parameter-Tuning-Deep-Learning-109c66b3094c4bf08af1710947a5ea88 Lecture Note - Parameter Tuning (Deep Learning) https://www.youtube.com/watch?v=1waHlpKiNyY&list=PLkDaE6sCZn6Hn0vK8co82zjQtt3T2Nkqc south-crown-f53.notion.site

Interpretation of the deep neural networks Deep neural networks can be challenging to interpret and understand with their multiple layers of nonlinear transformations. While they may produce highly accurate predictions, it can be challenging to determine how the model arrived at those predictions and which features of the input data were most important in making the prediction. This summarises a..

"Estimating Networks of Sustainable Development Goals" by Luis Ospina-Forero The paper "Estimating Networks of Sustainable Development Goals" by Luis Ospina-Forero focuses on the interlinkages and interactions between the 17 Sustainable Development Goals (SDGs) outlined by the United Nations. The paper presents a method for estimating networks of SDGs, which can be used to identify the most infl..

Ref1. Causal Discovery and Inference: concepts and recent methodological advances Notes [What to expect from this paper] Provides an overview of automated causal inference and emerging approaches to causal discovery from i.i.d data and time series Reviews fundamental concepts such as manipulations, causal models, sample predictive modelling, causal predictive modelling, and structural equation m..

Understanding Oracle SE and DBMS In today's digital age, maintaining and analyzing data is more crucial than ever. One of the ways that businesses can keep up with the demand is by using Oracle SE and DBMS to create and access databases. Oracle SE and DBMS allow users to create and connect an instance using SQLPLUS. This blog post will discuss the steps to create and access databases, machine le..

http://introtodeeplearning.com/ MIT Deep Learning 6.S191 MIT's introductory course on deep learning methods and applications. introtodeeplearning.com https://paperswithcode.com/sota/image-classification-on-cifar-10 Papers with Code - CIFAR-10 Benchmark (Image Classification) The current state-of-the-art on CIFAR-10 is ViT-H/14. See a full comparison of 235 papers with code. paperswithcode.com ht..