Submission & rebuttal in OpenReview
If you’re a coauthor and have not created an account in OpenReview, you should create an account after your college submits the paper to the site. This is because the reviews from the Reviewer will…
Kurious Fox
Residual plot for model diagnostic
Assessing assumptions like linearity, constant variance, error independence, and normal residuals is essential for linear regression. Residual plots visually assess the model’s goodness of fit, identifying patterns and influential data points. This post provides the…
Simple Linear Regression & Least square method
Simple linear regression is a statistical method to model the relationship between two continuous variables, aiming to predict the dependent variable based on the independent variable. The regression equation is Y = a + bX,…
Relation between the slope in linear regression and the covariance between x and y
The relationship between the slope in linear regression and the covariance between x and y is an important concept in statistics and data analysis. In simple linear regression, the slope of the regression line represents…
useful PowerPoint shortcuts
Some useful shortcuts in Microsoft PowerPoint that can enhance your productivity ??: General Shortcuts Slide Navigation Text Formatting Object and Shape Manipulation Presentation Mode View and Zoom These shortcuts can save you a lot of…
Tips for using WordPress
WordPress is a versatile platform that offers a wide array of features and functionalities. When using WordPress, it’s important to keep your site updated with the latest plugins and themes to ensure optimal performance and…
Model ensembling
Model ensembling combines multiple models to improve overall performance by leveraging diverse data patterns. Bagging trains model instances on different data bootstraps, while Boosting corrects errors sequentially. Stacking combines models using a meta-model, and Voting…
Using pipelines in Python/R to improve coding efficiency & readability
Pipelines in Python and R are powerful for structuring and processing data. In Python, Pandas and scikit-learn offer pipeline capabilities for data manipulation and machine learning workflows, while in R, the %>% operator from the…
How to export an R dataframe to LaTeX
The xtable package in R allows you to convert dataframes to LaTeX format. First, install and load the xtable package. Then, create or use an existing dataframe and convert it to LaTeX code using xtable.…
AI, Stat, Math, coding cheat sheets & learning songs
Free cheatsheets & learning tricks pandas-numpy-sklearn mnemonic cheat sheet Machine Learning & Deep Learning formulas & properties Basic probability and statistics formula sheet‘ classical missing data strategies clustering Set Identities with Intuitive Explanations Tips &…
Backpropagation Explained: A Step-by-Step Guide
Backpropagation is crucial for training neural networks. It involves a forward pass to compute activations, loss calculation, backward pass to compute gradients, and weight updates using gradient descent. This iterative process minimizes loss and effectively…
Gradient Descent Algorithm & Codes in PyTorch
Gradient Descent is an optimization algorithm that iteratively adjusts the model’s parameters (weights and biases) to find the values that minimize the loss function. The intuition behind gradient descent is learning how to move from…
Batch normalization & Codes in PyTorch
Batch normalization is a crucial technique for training deep neural networks, offering benefits such as stabilized learning, reduced internal covariate shift, and acting as a regularizer. Its process involves computing the mean and variance for…
Early Stopping & Restore Best Weights & Codes in PyTorch on MNIST dataset
When using early stopping, it’s important to save and reload the model’s best weights to maximize performance. In PyTorch, this involves tracking the best validation loss, saving the best weights, and then reloading them after…
Overfitting, Underfitting, Early Stopping, Restore Best Weights & Codes in PyTorch
Early stopping is a vital technique in deep learning training to prevent overfitting by monitoring model performance on a validation dataset and stopping training when the performance degrades. It saves time and resources, and enhances…
Learning Rate strategy & PyTorch codes
The learning rate is a hyperparameter that determines the size of the steps taken during the optimization process to update the model parameters. One can analogize it to riding a bike in a valley: Just…
2. Biến cố không thể xảy ra, biến cố đối
Ví dụ về biến cố không thể xảy ra Khi một biến cố không thể xảy ra, xác suất của nó sẽ bằng 0. Điều này có nghĩa là không có bất kỳ khả năng…
Biến cố và không gian mẫu là gì: Những ví dụ thú vị về Biến cố trong rừng
Trong lý thuyết xác suất, biến cố là một tập hợp các kết quả có thể xảy ra của một phép thử nào đó. Khi nói về các loài vật trong rừng, chúng ta có…
Admob Banner Positioning
To display Admob add at a given coordinate (x,y) on the display, you can use for example: However, it’s not a good idea to do that, because the resolution, camera position, canvas size can affect…
Quizzes: Mean Squared Error (MSE), Mean Absolute Error (MAE), and Root Mean Squared Error (RMSE)
Quiz on Mean Squared Error (MSE) Which of the following is the formula for MSE?A) B) C) D) In the context of MSE, what does represent?A) The predicted valueB) The observed valueC) The mean of…
Quizzes: stack generalization (stacking)
What’s stack generalization (stacking) in the world of machine learning? A) A way to combine multiple models to boost prediction accuracy ??B) A trick to shrink the feature space ?C) An algorithm to group data…
Quizzes: K-Means Clustering
In K-Means clustering, what does ‘K’ represent?A) The number of iterationsB) The number of clustersC) The distance metric usedD) The size of the dataset What’s the main goal of the K-Means clustering algorithm? A) Minimize…
Quizzes: Independent Component Analysis
What is the primary goal of ICA?A) To reduce the dimensionality of the datasetB) To separate a multivariate signal into additive, independent non-Gaussian componentsC) To standardize the dataD) To increase the number of features in…
Quizzes: Principal Component Analysis
PCA’s ultimate mission is to reduce dataset dimensionality. It can be used in both supervised and unsupervised learning tasks. These quizzes will test your knowledge on various aspects of PCA.
Quizzes: SVD for dimension reduction
Where does SVD shine for dimensionality reduction? A) Image compression ??B) Text mining ?C) Recommendation systems ?D) All of the above ? How does Singular Value Decomposition (SVD) come to the rescue for noise reduction?…
Feature selection versus dimensionality reduction
Feature selection and dimensionality reduction are two crucial processes in the field of machine learning. Feature selection involves choosing a subset of relevant features from the original set to improve model performance and reduce computational…
Bootstrap: introductory comics & quizzes
Bootstrap in machine learning refers to the process of resampling a dataset with replacement to assess the variability of a model’s performance. This technique is particularly useful when dealing with small datasets or when the…
Acc, precision, recall in detecting Rare “Silly Squirrel Syndrome” in Forest Animals
Consider the use of accuracy, precision, recall a funny classification problem in a medical scenario set in a forest involving forest animals. So, the data has: Total Forest Animals Tested: 1000Animals with Silly Squirrel Syndrome…
Bagging & Random Forest: intro & quizzes
Bagging, short for bootstrap aggregating, is a popular ensemble method in machine learning. It involves training multiple models, often decision trees, on different subsets of the training data and then combining their predictions to improve…
Quizzes on SVM (Support Vector Machines) and kernel tricks.
What is the objective of a Support Vector Machine?A) To find a hyperplane that separates data points into different classes with maximum margin.B) To minimize the error rate of classification.C) To find the nearest neighbors…
Quizzes: AIC, BIC, and Adjusted R-squared
AIC (Akaike Information Criterion) What is the main purpose of AIC in model selection?a) To maximize the number of predictors in a modelb) To identify the model with the lowest error ratec) To balance model…
Quizzes: forward, backward, and stepwise feature selection
What is the first step in forward feature selection?a) Removing the least important featureb) Adding the feature that provides the most significant improvement to the modelc) Adding all features and then removing them one by…