Remove 2013 Remove Metrics Remove Statistics Remove Testing
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Why you should care about debugging machine learning models

O'Reilly on Data

In addition to newer innovations, the practice borrows from model risk management, traditional model diagnostics, and software testing. Because ML models can react in very surprising ways to data they’ve never seen before, it’s safest to test all of your ML models with sensitivity analysis. [9]

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Towards optimal experimentation in online systems

The Unofficial Google Data Science Blog

the weight given to Likes in our video recommendation algorithm) while $Y$ is a vector of outcome measures such as different metrics of user experience (e.g., Experiments, Parameters and Models At Youtube, the relationships between system parameters and metrics often seem simple — straight-line models sometimes fit our data well.

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The AIgent: Using Google’s BERT Language Model to Connect Writers & Representation

Insight

In 2013, Robert Galbraith?—?an The most powerful approach for the first task is to use a ‘language model’ (LM), i.e. a statistical model of natural language. I tested several different flavors of BERT for use as synopsis classifiers before settling on the DistilBERT model from Hugging Face. an aspiring author?—?finished

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Credit Card Fraud Detection using XGBoost, SMOTE, and threshold moving

Domino Data Lab

In contrast, the decision tree classifies observations based on attribute splits learned from the statistical properties of the training data. Machine Learning-based detection – using statistical learning is another approach that is gaining popularity, mostly because it is less laborious. from sklearn import metrics.

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Build a RAG data ingestion pipeline for large-scale ML workloads

AWS Big Data

Each service implements k-nearest neighbor (k-NN) or approximate nearest neighbor (ANN) algorithms and distance metrics to calculate similarity. Ray cluster for ingestion and creating vector embeddings In our testing, we found that the GPUs make the biggest impact to performance when creating the embeddings. Waiting for connections.

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Deep Learning Illustrated: Building Natural Language Processing Models

Domino Data Lab

Although it’s not perfect, [Note: These are statistical approximations, of course!] Note: A test set of 19,500 such analogies was developed by Tomas Mikolov and his colleagues in their 2013 word2vec paper. This test set is available at download.tensorflow.org/data/questions-words.txt.]. Example 11.6 Note: Mikolov, T.,

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Using DataOps to Drive Agility and Business Value

DataKitchen

Chapin shared that even though GE had embraced agile practices since 2013, the company still struggled with massive amounts of legacy systems. One of the keys for our success was really focusing that effort on what our key business initiatives were and what sorts of metrics mattered most to our customers.

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