Linear algebra is a branch of mathematics which deals with solving a system of linear equations. It is widely used throughout science and engineering and it is essential to understanding machine learning algorithms. Linear algebra defines three basic data-structures - vectors, matrices and tensors, which are constantly used in machine and deep learning. In this … Continue reading Vectors, Matrices, Tensors – What’s The Difference?

# Category: Theory

# Activation functions explained

I love this kind of featured images. It makes people think that you are so smart and that you've figured out life. They're hilarious. It it makes the topic of the post seem like it's really deep, pun intended. If you've worked with neural networks even a little bit, you've probably come across the term activation function. … Continue reading Activation functions explained

# What Is Dropout? – Deep Learning

Overfitting can be a serious problem in deep learning. Dropout is a technique developed to solve this exact problem. It is one of the biggest advancements in deep learning to come out in the last few years. What is dropout? Dropout is a technique for addressing the overfitting problem. The idea is to randomly drop … Continue reading What Is Dropout? – Deep Learning