Example of Classifying 3D Figures With Tensorflow
Learn from this simple example about how to make a machine learning model for classifying 3D figures, in this case, from 3D MNIST dataset.
Tag: classification
How To Make a Speech Recognition Algorithm
Learn how to make a simple speech recognition algorithm using Google's machine learning library Tensorflow.
Sentiment Analysis On E-Commerce Dataset Example
Learn more about how to implement sentiment analysis in practice by following this example of binary classification on e-commerce dataset.
Transfer Learning And Hyperparameter Tuning Example
Learn more about how to implement transfer learning and hyperparameter tuning in this example for a classification task using images.
The Significance of Metrics in Machine Learning
Discover the importance of metrics in machine learning, common metrics and strategies for selecting them, and addressing limitations.
Binary Classification Example With Python
Learn more about how to make a binary classification model with python using sklearn, lazypredict, and grid search method.
The Perceptron: A Building Block of Neural Networks
Discover the role of a perceptron in neural networks, its mathematical foundation, limitations, and practical applications.
What is Confusion Matrix in Machine Learning
Learn about confusion matrix in machine learning for assessing classification models and enhancing prediction accuracy in your projects.
LDA in Machine Learning
Discover LDA in machine learning for dimensionality reduction, classification, and how it compares to PCA, SVM, and logistic regression.
Heart Disease Prediction Using Machine Learning
Explore machine learning for heart disease prediction with algorithms like logistic regression, CNN, and SVM for early detection.
Understanding the VC Dimension in Machine Learning
Explore the importance of VC dimension in machine learning, its implications for classifiers and how it impacts model performance.
Understanding Loss Functions in Machine Learning
Loss functions play a crucial role in machine learning, where they measure and guide models learning process to their optimal trained state.