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#### Plain and Simple Estimators

Machine learning is awesome, except when it forces you to do advanced math. The tools for machine learning have gotten dramatically better, and training your own model has never been easier. We’ll…

Read more at Towards Data Science#### Estimators

Advantages Similar to a tf.keras.Model , an estimator is a model-level abstraction. The tf.estimator provides some capabilities currently still under development for tf.keras . These are: Parameter se...

Read more at TensorFlow Guide#### A Guide to Estimator Efficiency

The efficiency of a statistical estimator is the ratio of the Cramer-Rao bound on variance to the actual variance in the estimator's predictions.

Read more at Towards Data Science#### The Consistent Estimator

A consistent estimator is one which produces a better and better estimate of whatever it is that it’s estimating, as the size of the data sample it is working upon goes on increasing.

Read more at Towards Data Science#### Performing Statistical Estimation

Statistics, as we know, is the study of gathering data, summarizing & visualizing the data, identifying patterns, differences, limitations and inconsistencies and extrapolating information regarding…

Read more at Towards Data Science#### Chapter 8 Estimation

The code for this chapter is in estimation.py . For information about downloading and working with this code, see Section 0.2 . 8.1 The estimation game Let’s play a game. I think of a distribution, an...

Read more at Think Stats#### Premade Estimators

Warning: Estimators are not recommended for new code. Estimators run v1.Session -style code which is more difficult to write correctly, and can behave unexpectedly, especially when combined with TF 2 ...

Read more at TensorFlow Tutorials#### Maximum Likelihood Estimation

The main goal of statistical inference is learning from data. However, data we want to learn from are not always available/easy to handle. Imagine we want to know the average income of American…

Read more at Analytics Vidhya#### Demystifying Estimation: The Basics

Know more about the population Table of Content 1. Introduction 2. Normal Distribution 3. Central Limit Theorem 4. Need of understanding the basics?? 5. Conclusion Introduction Statistics is an impor...

Read more at Towards AI#### Demystifying Estimation: The Basics

Know more about the population Photo by Roman Mager on Unsplash Table of Content 1. Introduction 2. Normal Distribution 3. Central Limit Theorem 4. Need to understand the basics? 5. Conclusion Introd...

Read more at Towards Data Science#### Estimation, Prediction and Forecasting

Estimation implies finding the optimal parameter using historical data whereas prediction uses the data to compute the random value of the unseen data. Estimation is the process of optimizing the…

Read more at Towards Data Science#### Let’s talk about estimation.

I struggled a lot with estimations in my two years as a developer. And I’m not the only one, most of my coworkers struggle with this too. I think it’s because nobody teaches you how to estimate when…

Read more at Level Up Coding#### Matching estimator is powerful, and simple

A challenge with many observational studies for obtaining causal effect is self-selection — in many cases, people choose to receive treatment for some reasons, and consequently, the treated people…

Read more at Towards Data Science#### Build a linear model with Estimators

Warning: Estimators are not recommended for new code. Estimators run v1.Session -style code which is more difficult to write correctly, and can behave unexpectedly, especially when combined with TF 2 ...

Read more at TensorFlow Tutorials#### A primer on statistical estimation and inference

A Primer on Statistical Estimation and Inference The law of large numbers and sound statistical reasoning are the foundation for effective statistical inference in data science Photo by Gabriel Ghnas...

Read more at Towards Data Science#### Estimate Smarter, not Harder

One of the most difficult (and paradoxically time-consuming aspects) of a software developer’s job is estimation. When developers make estimates about their work it can affect the success of the…

Read more at Better Programming#### Intro to Expectation Maximization

I’ve written a few posts on parameter estimation. The first post was on Maximum-Likelihood Estimation (MLE) where we want to find the value of some parameter θ renders the training data most likely…

Read more at Analytics Vidhya#### Statistics in ml

Learning is a never-ending process. Now, why would I say that in the very beginning?? Because Machine Learning is an emerging field where you can find endless information on any topic. So it’s always…...

Read more at The Pythoneers#### A Gentle Introduction to Estimation Statistics for Machine Learning

Last Updated on August 8, 2019 Statistical hypothesis tests can be used to indicate whether the difference between two samples is due to random chance, but cannot comment on the size of the difference...

Read more at Machine Learning Mastery#### A Gentle Introduction to Maximum Likelihood Estimation

The first time I heard someone use the term maximum likelihood estimation, I went to Google and found out what it meant. Then I went to Wikipedia to find out what it really meant. I got this: To…

Read more at Towards Data Science#### Statistical inference through confidence interval estimation

Sampling, Sampling Error, Non-sampling Error, Estimation, Confidence Interval, Standard Error

Read more at Towards Data Science#### Estimators revisited: Deep Neural Networks

In this episode of Cloud AI Adventures, learn how to train on increasingly complex datasets by converting a linear model to a deep neural network! As the number of feature columns in a linear model…

Read more at Towards Data Science#### Statistics

Undoubtedly, to be a top deep learning practitioner, the ability to train the state-of-the-art and high accurate models is crucial. However, it is often unclear when improvements are significant, or o...

Read more at Dive intro Deep Learning Book#### The Most Common Misinterpretations: Hypothesis Testing, Confidence Interval, P-Value

A refresher on how to interpret statistical inference correctly Continue reading on Towards Data Science

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