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Gradient clipping and Pytorch codes

Gradient clipping is a technique used to address the problem of exploding gradients in deep neural networks. It involves capping the gradients during the backpropagation process to prevent them from becoming excessively large, which can… 

Minibatch learning and variations of Gradient Descent

Minibatch learning in neural networks is akin to dancers learning a complex routine by breaking it down into smaller, manageable sections. This approach allows both the dancers and the neural network to focus on incremental… 

Giới thiệu về mạng nơ-ron

Ý tưởng về mạng nơ-ron Ý tưởng về mạng nơ-ron được lấy cảm hứng từ cấu trúc và chức năng của não, nơi các tế bào thần kinh được kết nối với nhau để xử… 

A Comical Introduction to Neural Network

The idea of neural networks is inspired by the structure and functioning of a brain, where interconnected neurons process and transmit information through complex networks. Neural networks have various applications, such as:Generating and telling jokes… 

Không gian Vector và ví dụ

Định nghĩa không gian vector Không gian vector (hay không gian tuyến tính) là một tập hợp các đối tượng (gọi là vector) cùng với hai phép toán cơ bản: 💡 Một không gian vector… 

Understanding Data Patterns: Trends, Cycles, and Clusters

This cute, funny comic helps us to understand what a pattern is. Data analysis relies on identifying patterns such as trends, cycles, and clusters to extract insights. Trends provide long-term behavioral insights influencing business strategies,… 

normal distribution comics & song

This song helps us better remember the properties of the normal distribution. A normal distribution, also known as a Gaussian distribution, is a symmetrical, bell-shaped continuous probability distribution characterized by its mean (?) and standard… 

Support Vector Machine + Python & R Codes

Support Vector Classifier (SVC) is a powerful algorithm for classification tasks, capable of handling linear and non-linear data using different kernel functions. It efficiently handles high-dimensional data for applications like image recognition and bioinformatics. Python… 

K-Means Clustering Method & Python Codes

K-Means Clustering is a popular unsupervised machine learning algorithm used for clustering data into groups. It is widely used in various fields such as image processing, market segmentation, and document clustering. The algorithm works by… 

What’s classification

Classification organizes items based on criteria. In data, it involves sorting into categories. It’s manual or automated with algorithms. Used in science, business, and technology to analyze and predict based on data. Crucial in document… 

Adjusted R squared

The coefficient of determination, or R-squared, measures how well an independent variable explains the variability of a dependent variable in a regression model. Its limitation lies in the fact that it does not decrease when… 

Feature selection & Model Selection

Feature selection involves identifying and including essential variables in the model, possibly leading to improved performance and interpretability. Adjusted R-squared is a common metric for regression analysis, addressing overfitting by penalizing unnecessary variables and offering… 

Multiple linear regression

Multiple linear regression is a powerful tool for modeling relationships between multiple independent variables and a single dependent variable. Let’s take a look at some examples with codes in Python and R to demonstrate its… 

Review: Maximum Likelihood Estimation

Maximum Likelihood Estimation (MLE) is a statistical method that estimates parameters by maximizing the likelihood function. For example, in a Poisson distribution, the MLE for the rate parameter ? is the sample mean. And here… 

Comparing forward, backward, stepwise feature selection

Forward selection adds features one by one, optimizing model performance but potentially missing the best subset. Backward selection starts with all features and removes the least significant, refining the model but being more computationally intensive.… 

A comic guide to underfitting

Underfitting in machine learning occurs when a model fails to capture underlying data patterns due to simplicity or insufficient training data. To address underfitting, select complex models, add features, and obtain more training data. Also,… 

Evaluation measure: MSE versus MAE, RMSE

This comic explains MSE and MAE, the commonly used evaluation metrics for regression. MSE emphasizes large deviations, while MAE provides a more robust measure when outliers are less significant. MSE is preferred as a loss… 

Parameters and Loss function

Machine learning parameters are values learned from training data to minimize prediction errors. For example, in a uniform distribution for bus arrival times, parameters $latex a$ and $latex b$ define the range. They are the… 

Unsupervised learning helps detect shady people

Unsupervised learning is a type of machine learning algorithm used to draw inferences from datasets consisting of input data without labeled responses. In unsupervised learning, the goal is to infer the natural structure present within… 

The model that’s not a girl & time machine

Comments: I already asked my student, and he confirmed that the reason he studied the ML class was because there was a model in that class ?. So, Mr. Fox left the class after he… 

Supervised learning: who’s supervising the forest?

Supervised learning involves training an algorithm on labeled data and pairing input with correct output. Unsupervised learning uses unlabeled data to find patterns. For example, predicting pizza delivery tips involves features like time, pizza type,… 

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