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Computer Vision (1)
Generative AI (2)
Machine Learning Basics (18)
Deep Learning (52)
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- DL Architectures (17)
  - Feedforward Network / MLP (2)
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  - Transformers (9)
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Natural Language Processing (27)
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Supervised Learning (115)
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Unsupervised Learning (55)
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Machine Learning Basics

Q. What is Machine Learning?
Q. What is the difference between Supervised and Unsupervised Learning
Q. Distinguish between Structured and Unstructured Data
Q. What is the Bias/Variance Tradeoff?
Q. What is Overfitting?
Q. What is Underfitting?
Q. How can overfitting be mitigated in a machine learning model?
Q. How can underfitting be mitigated?
Q. What are the subtypes of Cross Validation?
Q. How does a learning curve give insight into whether the model is under- or over-fitting?
Q. What are the different types of Gradient Descent?
Q. How does Machine Learning differ from Classical Statistics and Deep Learning?
Q. What is the Curse of Dimensionality?
Q. What is Gradient Descent?
Q. How does gradient descent differ from coordinate descent?
Q. How are model hyper-parameters generally selected?
Q. How does Cross Validation Work?
Q. What is a closed form solution, and what are the advantages of a problem having such a solution? Which algorithms have a closed form solution?

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Explore Questions by Topics

Computer Vision (1)
Generative AI (2)
Machine Learning Basics (18)
Deep Learning (52)
- DL Basics (16)
- DL Architectures (17)
  - Feedforward Network / MLP (2)
  - Sequence models (6)
  - Transformers (9)
- DL Training and Optimization (17)
Natural Language Processing (27)
- NLP Data Preparation (18)
Supervised Learning (115)
- Regression (41)
  - Linear Regression (26)
  - Generalized Linear Models (9)
  - Regularization (6)
- Classification (70)
  - Logistic Regression (10)
  - Support Vector Machine (9)
  - Ensemble Learning (24)
  - Other Classification Models (9)
  - Classification Evaluations (9)
Unsupervised Learning (55)
- Clustering (37)
  - Distance Measures (9)
  - K-Means Clustering (9)
  - Hierarchical Clustering (3)
  - Gaussian Mixture Models (5)
  - Clustering Evaluations (6)
- Dimensionality Reduction (9)
Statistics (34)
Data Preparation (35)
- Feature Engineering (30)
- Sampling Techniques (5)

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Other Questions in Machine Learning Basics

What is Regularization?
What is the effect of minimizing the within-cluster sum of squares on the shapes of clusters produced in K-Means?
What are some options for identifying the number of components in a GMM?
What are the key hyperparameters for a Random Forest model?
What are the two ways in which Hierarchical clustering can proceed?
Understanding the architecture of Recurrent Neural Networks (RNN)

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