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SLA vs SLO

An SLA is a formal document or statement that makes a commitment to customers. On the other hand, SLOs are the core content of an SLA that make specific and measurable commitments. SLA is a whole while SLOs are part of SLAs. An SLA contains many SLOs as target for metrics such as uptime and customer satisfaction rate. An SLA is a binding contract that is most legal wordage by content. On the other hand, SLO while also being binding to a certain extent there is no legality to this binding as is the case with SLA. via:  https://www.quora.com/What-is-the-difference-between-SLA-and-SLO
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Difference between genetic and evolutionary algorithms?

Genetic algorithms use crossover (hence the 'gene' in their name) and mutation to search the space of possible solutions. Evolutionary programming uses primarily mutation. As posted already, both are types of evolutionary algorithms.  So, evolutionary algorithms encompass genetic algorithms, and more. via: https://stackoverflow.com/questions/2890061/what-is-the-difference-between-genetic-and-evolutionary-algorithms
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Declarative vs Imperative Coding Paradigm

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Declarative is the what without the logic for the how. eg HTML, JSON Imperative is the logic flow, eg: Java, Python, any oops or functional programming language via: https://medium.com/front-end-weekly/imperative-versus-declarative-code-whats-the-difference-adc7dd6c8380 Imperatively speaking As a web developer you are likely using both paradigms with different code even if you may not be familiar with the terms. Programming languages tend to have  multi-paradigm  tendencies using both imperative and declarative syntax and I will use  JavaScript  to demonstrate that as well. Imperative paradigm Procedural  and  object-oriented programming  belong under  imperative paradigm  that you know from languages like  C ,  C++ ,  C# ,  PHP ,  Java  and of course  Assembly . Your code focuses on creating  statements that change program states  by creating algorithms that tell the computer  how to do things . It closely relates to how hardware works. Typically your code will ma
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tensorflow-crash-course

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http://nicklocascio.com/tensorflow-crash-course I was recently tasked with teaching a  lecture  on TensorFlow to ~200 MIT students in 6.864 (Deep Learning for NLP). This post contains everything I taught them – and more. “Literally the best presentation, ever.” - random student during my presentation to 6.864 (before they fainted) TensorFlow Fundamentals Computation Graphs Everything in TensorFlow comes down to building a computation graph. What is a computation graph? Its just a series of math operations that occur in some order. Here is an example of a simple computation graph: A SIMPLE COMPUTATION GRAPH This graph takes 2 inputs, (a, b) and computes an output (e). Each node in the graph is an operation that takes some input, does some computation, and passes its output to another node. We could make this computation graph in TensorFlow in the following way: a = tf . placeholder ( tf . float32 ) b = tf . placeholder ( tf . float32 ) c = tf . add ( a ,
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