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This is the simple application of a filter to an input that results in inactivation. Repeated application of the same filter to input results in a map of activations called a feature map, indicating the locations and strength of a ...

Machine Learning | Deep learning Machine Learning is a technique to learn from that data and then apply what has been learned to make an informed decision | The main difference between deep and machine learning is, machine learning models ...

Before discussing the different statistical tests, we need to get a clear understanding of what a null hypothesis is. A null hypothesis proposes that has no significant difference exists in the set of a given observation. Null: Two samples' mean is ...

In Machine Learning, there are various types of prediction problems based on supervised and unsupervised learning. These are classification, regression, clustering, and association. Here, we will discuss about classification and regression. Classification: In classification, we try to create a Machine Learning model ...

So basically there are 3 types of techniques: Supervised Learning: In this type of the Machine Learning technique, machines learn under the supervision of labeled data. Unsupervised Learning: Unlike supervised learning, it has unlabeled data. So, there is no supervision under which it works ...