Data Augmentation is an important step in the evolution of raw data into a practical and useable form for supervised learning. We review an example use case for the application of this technique.Read More
This is the era of artificial intelligence and machine learning, the applications we use in our daily life have gone from purely mobile to highly intelligence, but to gain this intelligence we are require a…Read More
Language modelling is the task of assigning a probability to sentences in a language. Besides assigning a probability to each sequence of words, the language models also assign a probability for the likelihood of a given word (or a sequence of words) to follow a sequence of words. Here we review and exercise of how Language Models work…Read More
A simple tutorial to analyse the sentiment of a book in Python and scikit-learn. In this tutorial, I will explain how to calculate the sentiment of a book through a Supervised Learning technique, based on Support Vector Machines (SVM).Read More
Before I explore the above regression techniques further. Let’s gain an understanding of the assumptions associated with regression techniques.
Supervised learning, Unsupervised learning, Reinforcement learning. These are the three most common ways of how machines can learn, therefore understanding their meaning and differences is important to know when getting started with artificial intelligence.Read More
Support Vector Machines explained with Python examples.Read More
Researchers borrowed equations from calculus to redesign the core machinery of deep learning so it can model a radical new Neural Network and continuous processes like changes in health.Read More