Kubeflow is a platform for data scientists who want to build and experiment with ML pipelines. Kubeflow is also for ML engineers and operational teams who want to deploy ML systems to various environments for development, testing, and production-level serving. Kubeflow is the ML toolkit for Kubernetes. The following diagram shows Kubeflow as a platform for arranging the components of your ML system on top of Kubernetes.

understanding AlexNet

3 minute read

Published: May 05, 2020

History of AlexNet

L2 and L1 Regularization in Machine Learning

3 minute read

Published: May 04, 2020

Regularization Definition

How Convolutional Neural Networks Work

14 minute read

Published: May 04, 2020

Nine times out of ten, when you hear about deep learning breaking a new technological barrier, Convolutional Neural Networks are involved. Also called CNNs or ConvNets, these are the workhorse of the deep neural network field. They have learned to sort images into categories even better than humans in some cases. If there’s one method out there that justifies the hype, it is CNNs.

convolution operation on images

5 minute read

Published: May 03, 2020

We all love to see beautiful images, but have you ever thought how do computers see an image? In this tutorial, we will give an explanation of how images are stored in a computer.

convolution

3 minute read

Published: May 02, 2020

Convolution Neural Network is one among various types of Deep Learning Neural Networks. CNN is very powerful and widely used in image classification, image recognition, computer vision etc.

machine learning and deep learning

2 minute read

Published: May 01, 2020

The term “artificial intelligence” has been around since the 1950s, but it’s taken more than half a century for it to finally have a transformative impact on everyday life. During one of those golden moments, in the 1980s, a branch of AI was born: machine learning (ML). ML uses mathematical algorithms that allow machines to learn. Machine learning is an analytical way of solving problems through identification, classification or prediction. Algorithms learn from entered data and then use this knowledge to draw conclusions from new data. Already in the 21st century, in 2011, a branch of machine learning called deep learning (DL) appeared. The popularity of machine learning and the development of the computing capacity of computers enabled this new technology. Deep learning as a concept is very similar to machine learning but uses different algorithms. While machine learning works with regression algorithms or decision trees, deep learning uses neural networks that function very similarly to the biological neural connections of our brain.