Gradient Descent is an optimization algorithm commonly used in machine learning and deep learning to minimize the loss function and find the optimal parameters (weights and biases) of a model. It’s based on the principle of iteratively moving in the direction of the steepest descent of the loss function with respect to the model parameters.…
TensorFlow is an open-source machine learning library developed by Google Brain team. It is one of the most popular frameworks for building and training machine learning and deep learning models. TensorFlow provides a comprehensive ecosystem of tools, libraries, and community resources to facilitate the development and deployment of various types of machine learning models. Key…
In the context of artificial neural networks (ANNs), MNIST refers to the MNIST dataset, which is often used as a benchmark for training and testing ANN models, particularly for image classification tasks. The MNIST dataset consists of a large collection of grayscale images of handwritten digits from 0 to 9. Each image is a 28×28…
A Multi-Layer Perceptron (MLP) is a type of artificial neural network that consists of multiple layers of nodes (perceptrons). Unlike a single-layer perceptron, an MLP has one or more hidden layers between the input and output layers. Each node in a layer is connected to every node in the subsequent layer. Here’s a basic overview…
A perceptron is one of the simplest forms of artificial neural networks. It’s a binary classifier that takes multiple binary inputs and produces a single binary output. Here’s how it works:
n Pandas library in Python pd.qcut is a function for performing quantile-based discretization of continuous variables. Quantile-based discretization involves dividing a continuous variable into discrete intervals or bins based on the distribution of its values. This process ensures that each bin contains approximately the same number of observations, making it useful for creating categories or…
RandomizedSearchCV is a method provided by scikit-learn for hyperparameter tuning and model selection through cross-validation. It’s similar to GridSearchCV, but instead of exhaustively searching through all possible combinations of hyperparameters, it randomly samples a fixed number of hyperparameter settings from specified distributions. Here’s a basic overview of how RandomizedSearchCV works: Here’s a basic example of…
get_params() is a method provided by scikit-learn estimators (such as classifiers, regressors, transformers, etc.) that returns a dictionary of the estimator’s parameters. These parameters are the hyperparameters that define the behavior of the estimator and can be tuned during the model selection or hyperparameter optimization process. Here’s a simple example of how you might use…
SimpleImputer is a class in scikit-learn, a popular machine learning library in Python, used for handling missing values in datasets. It provides a simple strategy for imputing missing values, such as filling missing entries with the mean, median, most frequent value, or a constant. Here’s a basic example of how you might use SimpleImputer: This…
Tomek Link Undersampling is a technique used to address class imbalance in machine learning datasets. It involves identifying Tomek links, which are pairs of instances from different classes that are nearest neighbors of each other, and removing instances from the majority class that form these links. The main idea behind Tomek Link Undersampling is to…
