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Introduction to Dimensionality Reduction for Machine Learning

The number of input variables or features for a dataset is referred to as its dimensionality. Dimensionality reduction refers to techniques that reduce the number...

ML approach to define antimalarial drug action

Abstract Drug resistance threatens the effective prevention and treatment of an ever-increasing range of human infections. This highlights an urgent need for new and improved...

Handling Big-p, Little-n (p >> n) in ML

What if I have more Columns than Rows in my dataset? Machine learning datasets are often structured or tabular data comprised of rows and columns. The...

Using Recursion to Traverse JSON Objects and the Filesystem

I work primarily on application-level programs, so I tend to not use recursion very often. However, every now and then I need a function...

Biases for distinct prospective decisions of self-performance

Abstract Metacognition can be deployed retrospectively -to reflect on the correctness of our behavior- or prospectively -to make predictions of success in one’s future behavior...

If Tool For Machine Learning Model Investigation

Machine learning era has reached the stage of interpretability where developing models and making predictions is simply not enough any more. To make a powerful impact...

Quality Metrics for Deep Neural Networks

    We introduce the weightwatcher (ww) , a python tool for a python tool for computing quality metrics of trained, and pretrained, Deep Neural Netwworks. pip install weightwatcher This...

Classification Tasks in Machine Learning

Machine learning is a field of study and is concerned with algorithms that learn from examples. Classification is a task that requires the use of...

Clustering Algorithms With Python

Clustering or cluster analysis is an unsupervised learning problem. It is often used as a data analysis technique for discovering interesting patterns in data, such as groups of...

Introducing Argmax in Machine Learning

Argmax is a mathematical function that you may encounter in applied machine learning. For example, you may see “argmax” or “arg max” used in a...

Statistical Mechanics of Deep Neural Networks

Introduction For the past year or two, we have talked a lot about how we can understand the properties of Deep Neural Networks by examining...

Explaining Support Vector Machines (SVM)

One of the most prevailing and exciting supervised learning models with associated learning algorithms that analyse data and recognise patterns is Support Vector Machines...
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SwiftUI TabView Introduction and Tab Bar Customization

  The tab bar interface appears in some of the most popular mobile apps such as Facebook, Instagram, and Twitter. A tab bar appears at...

Preparing Data in Machine Learning

Data preparation may be one of the most difficult steps in any machine learning project. The reason is that each dataset is different and highly...

Using blockchain to crack down abusive imagery

Blockchain could be an effective and efficient solution for helping to rid the internet of abusive imagery. Tackling abusive imagery can help victims...

Transforming chatbots throgh AI and ML

The increasing technology has always been a saviour for us. Technology still provides us with solutions for existing problems. One of the answers offered...
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