How To Forecast With Moving Average Models
Tutorial and theory on how to carry out forecasts with moving average models for time series analysis- 28561Murphy ≡ DeepGuide
A Visual Learner's Guide to Explain, Implement and Interpret Principal Component Analysis
Linear Algebra for Machine Learning - Covariance Matrix, Eigenvector and Principal Component- 26136Murphy ≡ DeepGuide
Quantile Loss & Quantile Regression
Learn how to adjust regression algorithms to predict any quantile of data- 25726Murphy ≡ DeepGuide
Multinomial Logistic Regression in R
Statistics in R Series- 24804Murphy ≡ DeepGuide
Primer on Bayesian Deep Learning
Probabilistic Deep Learning- 21194Murphy ≡ DeepGuide
What is ARIMA?
An introduction to the ARIMA forecasting model and how to use it for time series- 25688Murphy ≡ DeepGuide
Fundamentals of Statistics All Data Scientists & Analysts Should Know – With Code –
This article is a comprehensive overview of the fundamentals of statistics for Data Scientists and Data Analysts.- 22850Murphy ≡ DeepGuide
Understanding Causal Trees
CAUSAL DATA SCIENCE How to use regression trees to estimate heterogeneous treatment effects Cover, image by Author In causal inference, we are usually interested in estimating the causal effect of a treatment (a drug, ad, product, …) on an outcome o- 21103Murphy ≡ DeepGuide
Building Blocks of Causal Inference – A DAGgy approach using Lego
An Introduction to Causal Inference with DAGs and Bayesian Regression- 25604Murphy ≡ DeepGuide
Simulated Annealing with Restart Strategy
A variation on the classic Simulated Annealing optimisation algorithm and its application to the Travelling Salesman Problem- 26598Murphy ≡ DeepGuide
A New Way to Predict Probability Distributions
Exploring multi-quantile regression with Catboost- 27230Murphy ≡ DeepGuide
How To Solve Travelling Salesman Problem With Simulated Annealing
Getting the optimal solution to the Travelling Salesman Problem using the Simulated Annealing optimisation algorithm- 29313Murphy ≡ DeepGuide
Uncovering the Limitations of Traditional DiD Method
Dealing with Multiple Time Periods and Staggered Treatment Timing- 26824Murphy ≡ DeepGuide
How strongly associated are your variables?
Use Cramer's V test to check how strongly associated are two categorical variables- 29532Murphy ≡ DeepGuide
Breaking Linearity With ReLU
Explaining how and why the ReLU activation function is non-linear- 21176Murphy ≡ DeepGuide
Coupon Collector's Problem: A Probability Masterpiece
Unpacking the intricacies of a classic probability puzzle- 28717Murphy ≡ DeepGuide
Full Explanation of MLE, MAP and Bayesian Inference
Introducing maximum likelihood estimation, maximum a posteriori estimation and Bayesian Inference- 25389Murphy ≡ DeepGuide
Another (Conformal) Way to Predict Probability Distributions
Conformal multi-quantile regression with Catboost- 26160Murphy ≡ DeepGuide
A Complete Tutorial on Off-Policy Evaluation for Recommender Systems
How to reduce the offline-online evaluation gap- 23816Murphy ≡ DeepGuide
What is Tabu Search?
An intuitive explanation of the Tabu Search optimization algorithm and how to apply it to the traveling salesman problem- 27543Murphy ≡ DeepGuide
We look at an implementation of the HyperLogLog cardinality estimati
Using clustering algorithms such as K-means is one of the most popul
Level up Your Data Game by Mastering These 4 Skills
Learn how to create an object-oriented approach to compare and evalu
When I was a beginner using Kubernetes, my main concern was getting
Tutorial and theory on how to carry out forecasts with moving averag