PyTorch – A Savior Deep Learning Framework
In this article, I am going to discuss why PyTorch is the best Deep Learning framework. It’s extremely easy to use and very flexible for implementations.
Methods
Nearest Neighbor Classifier – A Working Example
In this post, we present a working example of the k-nearest neighbor classifier. Previously we covered the theory behind this algorithm. Please refer Nearest Neighbor Classifier – From Theory to Practice post for further detail. A Recap to Nearest Neighbor Classifier When we utilize KNN for classification purposes, the prediction is the class associated the highest frequency …
Understand the Powerful Supervised Machine Learning in a Simple Way
In this article, we focus on the notion of supervised machine learning and its associated categories. We briefly discuss the popular categories and algorithms without digging too much into detail. The goal is to have an idea of what is supervised learning.
How to Frame, Organize and Manage a Competitive Machine Learning Project?
In this article, we outline different steps to define, frame, organize, deploy, and evaluate a successful Machine Learning (ML) project. The expected audience for this article involves business stakeholders, supervisors, Machine Learning experts, and software development engineers.
Effortless Logistic Regression Using TensorFlow
In this tutorial, we described logistic regression and represented how to implement it in code. Instead of making a decision based on the output probability based on a targeted class, we extended the problem two a two class problem in which for each class we predict the probability.
Linear Regression using TensorFlow
In this tutorial, we walked through the linear model creation using TensorFlow. The line which was found after training, is not guaranteed to be the best one. Different parameters affect the convergence accuracy. The linear model is found using stochastic optimization and its simplicity makes our world easier.
Nearest Neighbor Classifier – From Theory to Practice
The K-nearest neighbors (KNNs) classifier or simply Nearest Neighbor Classifier is a kind of supervised machine learning algorithms which operates based on spatial distance measurements. In this post, we investigate the theory behind it.