Mathematics is the core of many sciences, including machine learning and data science. But how exactly does it help us solve such complex problems? What happens behind the scene that ...

In the previous tutorials, we have talked about various neural nets architectures. In this tutorial, we will learn about Autoencoders and their application in machine learning. As we already know, ...

In recent years, convolutional neural networks (CNNs) have revolutionized the field of computer vision, enabling machines to recognize patterns, classify images, and even generate artwork. Among the numerous advancements in ...

In the previous tutorial on Introduction to Artificial Neural Networks (ANNs), we explored the fundamentals of deep learning and witnessed the capabilities of fully connected neural networks in tackling various ...

TensorFlow is an open-source machine learning library developed by the Google Brain team. It provides a comprehensive set of tools and capabilities for building and deploying various machine learning and ...

In our previous tutorials, we explored different machine-learning tools to solve problems like predicting house prices or classifying emails. But what if we want computers to understand the world more ...

When should we be afraid of AI replacing humans? How can we discern whether AI possesses self-consciousness and thinking abilities? Is consciousness present in current AI systems, if yes, how ...

Gaussian Mixture Models related concepts are explained in the notebook below. Please practice and ask your questions in the forum if any. Please don't share the notebook anywhere without our ...

In this tutorial, we will learn our next machine-learning model called nearest neighbors (NN), known as k-nearest Neighbors (k-NN). It belongs to the family of supervised learning algorithms, specifically within ...