lazy-evaluation
Lazy evaluation is a programming technique where expressions are not evaluated until their results are actually needed. This approach contrasts with eager evaluation, where expressions are computed immediately upon being encountered. By deferring computations, lazy evaluation can enhance performance and reduce memory usage, particularly when dealing with large datasets or complex operations. In languages like Python, lazy evaluation can be implemented using generators, the iterator protocol, and libraries designed for this purpose. This method allows for the creation of infinite data structures and can lead to more efficient resource management in applications.
Exploring Lazy Evaluation in Python
L azy evaluation is a programming concept where expressions or computations are not evaluated immediately, but are delayed until their results are actually needed. This can improve performance and mem...
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A Nibble of Lazy Evaluation
Eager vs strict vs non-strict vs lazy — what does it all mean? I envy koalas for their ability to be supremely comfortable in the most awkward of places. Photo by David Clode on Unsplash Nibble: a sm...
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What's Lazy Evaluation in Python?
This tutorial explores lazy evaluation in Python and looks at the advantages and disadvantages of using lazy and eager evaluation methods. By the end of this tutorial, you'll clearly understand which ...
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Unleash the power of lazy evaluation!
Unleash the power of lazy evaluation! Dive into Python Generators and master iterators with yield. 🚀🧩 Python Generators are a special class of functions in Python that return an iterable set of ite...
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3 Reasons Why Spark’s Lazy Evaluation is Useful
Spark’s Lazy Evaluation and Why You Should Care Continue reading on Towards Data Science
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Lazy Evaluation using Recursive Python Generators
Can recursive functions use "lazy evaluation"? - Yes, they can - Using Python's generator functions!
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Understanding Lazy Evaluation in Polars
Understand what is eager and lazy execution and how you can use lazy execution to optimize your queries Continue reading on Towards Data Science
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The trouble with evaluating anything
It is very hard to evaluate people’s productivity or work in any meaningful way. This problem is the source of: Consternation about peer review The reason why post publication peer review doesn’t work...
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Lazy introduction to laziness in R
In the programming world, laziness can often be a good thing: it is both a human quality that can motivate automation efforts, and a programming concept that avoids wasting resources such as memory. N...
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LazyModuleMixin
A mixin for modules that lazily initialize parameters, also known as “lazy modules.” Modules that lazily initialize parameters, or “lazy modules”, derive the shapes of their parameters from the first ...
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Using LazyPredict for Evaluating ML Algorithms
Evaluating machine learning algorithms is a common task performed by data scientists. While a data scientist needs to know the different types of machine learning algorithms to use for different…
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LazyLinear
A torch.nn.Linear module where in_features is inferred. In this module, the weight and bias are of torch.nn.UninitializedParameter class. They will be initialized after the first call to forward is do...
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