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Machine Learning — Core Concepts
par Jude · 14/05/2026 · 11 vues · partages
Supervised learning, regularization, gradient descent, overfitting, classical models. Great prep for associate-level ML exams (AWS ML / GCP ML Engineer).
30
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🇬🇧 English
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🔴 Difficile
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Aperçu des questions
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1. Statement 1| Linear regression estimator has the smallest variance among all unbiased estimators.
Statement 2| The coefficients α assigned to the classifiers assembled by AdaBoost are always non-negative. -
2. Statement 1| RoBERTa pretrains on a corpus that is approximate 10x larger than the corpus BERT pretrained on.
Statement 2| ResNeXts in 2018 usually used tanh activation functions. -
3. Statement 1| Support vector machines, like logistic regression models, give a probability distribution over the possible labels given an input example.
Statement 2| We would expect the support vectors to remain the same in general as we move from a linear kernel to higher order polynomial kernels. -
4. A machine learning problem involves four attributes plus a class. The attributes have 3, 2, 2, and 2 possible values each. The class has 3 possible values. How many maximum possible different examples are there?
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5. As of 2020, which architecture is best for classifying high-resolution images?
… et 25 autres questions.