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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…
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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...
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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.
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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.
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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…
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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...
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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 ...
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Maximum Likelihood Estimation of Parameters for Random Variables
C oncepts in probability and statistics can be somewhat elusive due to the combination of high level mathematics, bad notation, and entanglement of random variables and data. This article sheds light ...
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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…
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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...
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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...
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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…
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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…
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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…
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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...
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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…
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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…
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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…...
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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...
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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…
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Statistical inference through confidence interval estimation
Sampling, Sampling Error, Non-sampling Error, Estimation, Confidence Interval, Standard Error
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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…
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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...
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