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Gradient and gradient descent
- Derivative
- Derivative and partial derivative
- Derivative and directional derivative
- Derivative and gradient
- Gradient descent algorithm
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Normal equation
- Vector addition and subtraction
- Vector dot product (scalar product, inner product)
- Vector cross product
- Normal equation
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Logistic regression (binomial regression) and regularization
- Experience scipy.optimize.fmin_tnc
- Regularization
- Norm of vector and matrix
- Dataset features expansion/extraction
- When a lower dimensional space NOT discriminable dataset is PROJECTED to a PROPER higher dimensional space it always will be discriminable, the boundary is a hyper plane or just a discrimination function.
- Model accuracy comparison between 10-dimensional and 6-dimensional
- 'linear_model.LogisticRegression' with sklearn
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GLM and exponential family distributions
- Bernoulli distribution in GLM form
- Gaussian distribution (normal distribution) in GLM form
- Softmax regression (multinomial logistic regression) (categorical distribution (variant 3)) in GLM form
- GLM β linear regression
- GLM β logistic regression
- Why the PMF for categorical distribution(special form of multinomial distribution: k > 2 and n = 1) has no coefficient like the multinomial distribution's PMF
- How to use the table here to build GLM quickly
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Neural network (hand-written digits recognition)
- What's a neural network
- One-hot encoding
- Forward propagation
- Backpropagation algorithm
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SVM
- Functional margin vs geometric margin, and SVM interpretation
- Lagrange multiplier, Lagrange duality and KKT conditions
- Coordinate ascent algorithm and SMO
- Slack variables and penalty factors
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PCA
- What does PCA do?
- Another interpretation of what does PCA do
- What are the application scenarios of PCA?
- PCA pseudo code
- Eigenvalues and eigenvectors
- Singular value and Singular Value Decomposition
- PCA dimensionality reduction example
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Some generic questions
- 1. Type 1 error, type 2 error, power of test
- 2. Over-fitting, under-fitting
- 6. How is k-NN different from k-means clustering?
- 7. p-value
- 12. What is naive in a Naive Bayes?
- 13. How can you select k for k-means?
- 14. How to measure a model?
- 18. What is cross-validation?
- 19. What is bias-variance trade off?
- 20. The types of biases that occur during sampling?
- 21. What is the confusion matrix?
- 22. What are exploding/vanishing gradients?
- 25. What is eigenvectors and eigenvalues?
- 27. What are AutoEncorders?
- 28. How do you avoid the over-fitting during the training?
- 29. What are the differences among standardization, normalization and regularization?
- 30. Some feature selection methods used to select the right variables
- 33. MAE vs MSE vs RMSE
- 34. How can outlier values be treated?
- 36. What is a Box-Cox Transformation?
- 37. What is the hyperbolic tree?
- 39. What's the difference between probability and likelihood?
- 40. What cross-validation technique would you use on a time series dataset?
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Ensemble learning
- What is bootstrapping (bootstrap sampling)?
- Why do we need bootstrap sampling?
- Implement Bootstrap Sampling in Python
- What is ensemble learning
- Boosting
- Bagging
- Stacking
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Gradient(GBM) vs AdaBoost vs XGBoost
- Decision tree vs random forest
- Random forest vs gradient boosting
- Gradient vs AdaBoost
- XGBoost
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Optimizers
- Gradient descent
- Types of gradient descent
- Role of an optimizer
- Types of optimizers
- Momentum
- Nesterov accelerated gradient(NAG)
- Adagrad β Adaptive Gradient Algorithm
- Adadelta
- RMSProp
- Adam β Adaptive Moment Estimation
- Nadam- Nesterov-accelerated Adaptive Moment Estimation
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np.array([0, 0])
vsnp.array([0., 0.])
>>> import numpy as np >>> t = np.array([0, 0]) >>> t[0] = 0.97 >>> t array([0, 0]) >>> t[0] = 1.97 >>> t array([1, 0]) >>> t = np.array([0., 0.]) >>> t[0] = 0.97 >>> t array([0.97, 0. ]) >>> t = np.array([0, 0.]) >>> t[0] = 0.97 >>> t array([0.97, 0. ])