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This article explains what parallelism is and how Jest can help you run your tests faster.
Let’s say that you have 4 test files which take one second each, sequentially running them would take, in total, four seconds. As the number of test files increase, so does the total execution time.
To speed-up your tests, Jest can run them in parallel. By default, Jest will parallelise tests that are in different files.
IMPORTANT: Paralellising tests mean using different threads to run test-cases simultaneously. …
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This article covers
● Modeling algebraic expressions as data structures in Python
● Writing code to analyze, transform, or evaluate an algebraic expression
● Building a data structure from elements and combinators
In Python and other languages, we often think of functions as mini-programs. They’re self-contained sets of instructions that accept some input data, do some ordered computations with it, and identify some result value as an output. From the perspective of functional programming, we consider functions to be data that we can compute things about. …
This article discusses getting started with baselines and generalized linear models.
Neural networks are the most important class of machine learning algorithms for handling perceptual problems such as computer vision and NLP. Thus, it is the most important class of models for the subject covered by this book.
In this post, we will train two representative pretrained neural network language models on the two illustrative example problems we have been baselining in this chapter. …
Andrew Ferlitsch, from the developer relations team at Google Cloud AI, is so far out on the cutting edge of machine learning and artificial intelligence that he has to invent new terminology to describe what’s happening in Cloud AI with Google Cloud’s enterprise clients. In this interview with editors at Manning Publications, he talks about the current and coming changes in machine learning systems, starting with the concept of model amalgamation. Ferlitsch is currently writing a book, Deep Learning Design Patterns, which collects his ideas along with the most important composable model components.