List: ML: statistics | Curated by Antonio Esteves

Jan 27, 2025
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ML: statistics
In
Level Up Coding
by
Ari Joury, PhD
Every Data Scientist Should Know About Bayesian NetworksExploring causalities where correlations are not enough
Jan 21
4
Jan 21
4
In
Towards AI
by
Shenggang Li
Bayesian Methods: From Theory to Real-World ApplicationsA Practical Guide to Using Bayesian Techniques in A/B Testing and Uplift Modeling
Sep 29, 2024
Sep 29, 2024
In
Level Up Coding
by
Arthur Mello
Causal Inference: an OverviewFind out when correlation actually means causation
Oct 13, 2024
1
Oct 13, 2024
1
In
Towards AI
by
Shenggang Li
Decoding Latent Variables: Comparing Bayesian, EM, and VAE ApproachesA Deep Dive into Mathematical Foundations, A/B Testing Applications, and Choosing the Right Method for Your Data Challenges.
Dec 14, 2024
1
Dec 14, 2024
1
In
Tech Spectrum
by
Aarafat Islam
Causal Inference in AIWhat happens if we change one variable, and how does it impact other variables?
Dec 13, 2024
Dec 13, 2024
Deepak Shah
Deep Learning Notes Part IIRestricted Boltzmann Machine (RBM)
Feb 25, 2017
Feb 25, 2017
Justin Milner
Deep Belief Networks and Deep Boltzmann Machines (Unsupervised Learning, article #4)Video form of this article can be found on my youtube channel: https://www.youtube.com/watch?v=eukNv0-1E2w&t=5s
Jul 13, 2023
Jul 13, 2023
In
The Startup
by
Prince Canuma
What Are RBMs, Deep Belief Networks and Why Are They Important to Deep Learning?In this article, we are going to take a look at what are DBNs and where can we use them.
Dec 18, 2020
Dec 18, 2020
In
DataDrivenInvestor
by
Renu Khandelwal
Deep Learning — Deep Belief Network (DBN)In this post we will explore what are the features of Deep Belief Network(DBN), architecture of DBN and how DBN’s are trained and it’s…
Dec 13, 2018
1
Dec 13, 2018
1
In
Towards AI
by
Andrea Berdondini
A Modern Approach To The Fundamental Problem of Causal InferenceABSTRACT: The fundamental problem of causal inference defines the impossibility of associating a causal link to a correlation, in other…
Nov 14, 2024
1
Nov 14, 2024
1
In
Stackademic
by
Okan Yenigün
Bayesian Linear Regression: Merging Probability with Predictive ModelingUncertainty in Focus: Exploring Linear Regression with Bayesian Methods
Oct 6, 2024
Oct 6, 2024
In
TDS Archive
by
Erdogan Taskesen
How to Find the Best Theoretical Distribution for Your DataKnowing the underlying data distribution is an essential step for data modeling and has many applications, such as anomaly detection…
Feb 3, 2023
15
Feb 3, 2023
15
In
TDS Archive
by
Lukasz Szubelak
Causal Inference with Python: A Guide to Propensity Score MatchingAn introduction to estimating treatment effects in non-randomized settings using practical examples and Python code
Jul 2, 2024
4
Jul 2, 2024
4
In
Towards AI
by
Joseph Robinson, Ph.D.
The Fundamental Mathematics of Machine LearningA Deep Dive into Vector Norms, Linear Algebra, Calculus
Jul 26, 2024
13
Jul 26, 2024
13
In
TDS Archive
by
Khin Yadanar Lin
What Is Causal Inference?A beginner’s guide to causal inference methods: randomized controlled trials, difference-in-differences, synthetic control, and A/B testing
Jul 22, 2024
Jul 22, 2024
Jonathan Hui
Kernels for Machine LearningIn many machine learning problems, input data is transformed into a higher-dimensional feature space using a non-linear mapping to make it…
May 27, 2024
4
May 27, 2024
4
In
Lunit Team Blog
by
HyunJae Lee
Bayesian Optimization Meets Self-DistillationBOSS combines Bayesian Optimization and Self-Distillation allowing for more efficient hyperparameter tuning and enhanced knowledge…
May 8, 2024
May 8, 2024
R Train Data
Resampling Distributions in python, Bootstrap, Jackknife, Monte CarloA probability distribution shows us the probabilities of different outcomes occurring. This distribution is useful because it describes the…
Mar 4, 2024
1
Mar 4, 2024
1
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In
Artificial Intelligence in Plain English
by
Cristian Leo
The Math Behind Naive Bayes ClassifierUnveiling the Simplicity, Efficiency, and Versatility of Naive Bayes Classificationction
Nov 27, 2023
2
Nov 27, 2023
2