Are you interested in learning how to use Keras? Do you already have an understanding of how neural networks work? Check out this lean, fat-free 7 step plan for going from Keras newbie to master of its basics as quickly as is possible.
DBSCAN clustering can identify outliers, observations which won’t belong to any cluster. Since DBSCAN clustering identifies the number of clusters as well, it is very useful with unsupervised learning of the data when we don’t know how many clusters could be there in the data.
In neural networks, connection weights are adjusted in order to help reconcile the differences between the actual and predicted outcomes for subsequent forward passes. But how, exactly, do these weights get adjusted?
A complete video guide to Machine Learning in R! This great compilation of tutorials and lectures is an amazing recipe to start developing your own Machine Learning projects.
This post includes 5 specific video-based options for furthering your understanding of neural networks and deep learning, collectively consisting of many, many hours of insights.
What is an artificial neural network? How does it work? What types of artificial neural networks exist? How are different types of artificial neural networks used in natural language processing? We will discuss all these questions in the following article.
Random Forest, one of the most popular and powerful ensemble method used today in Machine Learning. This post is an introduction to such algorithm and provides a brief overview of its inner workings.
In this article we focus on the personalization aspect of model building and explain the modeling principle as well as how to implement Photon-ML so that it can scale to hundreds of millions of users.
Edge analytics is the collection, processing, and analysis of data at the edge of a network either at or close to a sensor, a network switch or some other connected device.
In this article, we aim to discuss various GLMs that are widely used in the industry. We focus on: a) log-linear regression b) interpreting log-transformations and c) binary logistic regression.
Get your productivity boosted with Hadley Wickham's powerful R package, tidyverse. It has all you need to start developing your own data science workflows.
Fake news is an important issue on social media. This article provides an overview of fake news characterization and detection in Data Science and Machine Learning research.
Looking at the strengths of a neural network, especially a recurrent neural network, I came up with the idea of predicting the exchange rate between the USD and the INR.
The use case involved is to predict the end life of large industrial batteries, which falls under the genre of use cases called preventive maintenance use cases.
Employee attrition (churn) is a major cost to an organization. We recently used two new techniques to predict and explain employee turnover: automated ML with H2O and variable importance analysis with LIME.
Looking to boost your machine learning competitions score? Here’s a brief summary and introduction to a powerful and popular tool among Kagglers, XGBoost.
Machine learning algorithms aren’t difficult to grasp if you understand the basic concepts. Here, a SAS data scientist describes the foundations for some of today’s popular algorithms.
7 Steps to Mastering Deep Learning with Keras
Top 10 Machine Learning Algorithms for Beginners
Understanding Machine Learning Algorithms
| 