HDF5 Data Format Introduction© Karobben

HDF5 Data Format Introduction

HDF5 (Hierarchical Data Format version 5) is a file format designed for efficiently storing and organizing large, complex datasets. It uses a hierarchical structure of **groups** (like directories) and **datasets** (like files) to store data, supporting multidimensional arrays, metadata, and a wide variety of data types. Key advantages include **compression**, **cross-platform compatibility**, and the ability to handle large datasets that don’t fit in memory. It’s widely used in fields like scientific computing, machine learning, and bioinformatics due to its efficiency and flexibility.
Read more

Softmax

Softmax is a mathematical function commonly used in machine learning, particularly in the context of classification problems. It transforms a vector of raw scores, often called logits, from a model into a vector of probabilities that sum to one. The probabilities generated by the softmax function represent the likelihood of each class being the correct classification. $$\sigma(\mathbf{z})_i = \frac{e^{z_i}}{\sum_{j=1}^K e^{z_j}}$$
Read more

Support Vector Machine

Support Vector Machine (SVM) is a supervised learning algorithm used for classification and regression. It finds the best hyperplane that separates the data into different classes with the largest possible margin. SVM can work well with high-dimensional data and use different kernel functions to transform data for better separation when it is not linearly separable.$$f(x) = sign(w^T x + b)$$
Read more

Random Forest

Random Forest is an ensemble machine learning algorithm that builds multiple decision trees during training and merges their outputs to improve accuracy and reduce overfitting. It is commonly used for both classification and regression tasks. By averaging the predictions of several decision trees, Random Forest reduces the variance and increases model robustness, making it less prone to errors from noisy data. $$\text{Entropy}_{\text{after}} = \frac{|S_l|}{|S|}\text{Entropy}(S_l) + \frac{|S_r|}{|S|}\text{Entropy}(S_r)$$
Read more
Kernel Density Estimation (KDE)© Karobben

Kernel Density Estimation (KDE)

Kernel Density Estimation (KDE) is a non-parametric method to estimate the probability density function (PDF) of a random variable based on a finite set of data points. Unlike parametric methods, which assume that the underlying data follows a specific distribution (like normal, exponential, etc.), KDE makes no such assumptions and can model more complex data distributions.$$ \hat{f}(x) = \frac{1}{n \cdot h} \sum_{i=1}^{n} K\left(\frac{x - x_i}{h}\right) $$
Read more
Simulated Annealing (SA)© Karobben

Simulated Annealing (SA)

Simulated Annealing (SA) is a probabilistic technique used for finding an approximate solution to an optimization problem. It is particularly useful for problems where the search space is large and complex, and other methods might get stuck in local optima.
Read more
Multi-layer Neural Nets© Karobben
Hidden Markov Model© Karobben
Artificial Intelligent 1© Karobben
Evaluating the quality of classification© Dell-3