Multi-task Learning

Introduction Multi-task learning is the sub-branch of machine learning. In which, many learning tasks are resolved together using commonalities and changes through tasks. When related to training the models alone, this may outcome in enhanced learning effectiveness and estimate correctness for the task-specific models. Multitask Learning is a method of inductive transfer. It enhances generalization […]

CNN image classification

Introduction The dataset between Dogs and cats is a standard computer vision dataset. It involves classifying prints as either containing a canine or cat. Though the problem sounds easy, it was only effectively addressed in the last many times using deep literacy convolutional neural networks. While the dataset is practically answered. It may be used as the base for literacy and rehearsing how to develop, estimate, and use convolutional deep literacy neural networks for image bracket from scrape. This comprises; How to improve a robust test harness for predicting the efficiency of the model. How to explore advancements to […]

Tree Based Machine Learning Algorithms

Introduction Tree-based algorithms are well-thought-out to be the best and widely used supervised learning methods. They allow predictive models with high accurateness, strength, and ease of interpretation. They map non-linear relationships to a certain extent well. They are flexible at solving any kind of problem at hand related to classification or regression. This article purposes […]

Handling Imbalanced Datasets

Introduction In machine learning classification, imbalanced classes are a common problem. There is an uneven ratio of observations in each class. The dataset pre-processing is maybe the most significant step in building a Machine Learning model. In this article, we will understand that how to deal with categorical variables such as missing values and to […]

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