Knowledge graphs are network graphs that link related concepts and properties together to create a form of inferencing engine, with knowledge engineering being the programming aspect of graph usage. Explore how knowledge graphs are created and queried, how they are used as part of a broader form of enterprise metadata management, and how they tie into ML and the IoT.
Table of contents: What is Deep Learning? Supervised DL: The KISS Pathway That Leads To The Expected Unsupervised Deep Learning: An Exploratory Journey To Figuring… Read More »The Convoluted World of Supervised vs Unsupervised Learning
In the era of the Internet, the ability to crunch large amounts of data and process it with speed and efficiency has become essential for… Read More »8 Most-Used Data Science and Machine Learning Services for 2020
Summary: Advances in very low cost compute and Model Based Reinforcement Learning make this modeling technique that much closer to adoption in the practical world.… Read More »Reinforcement Learning Starts to Deliver on Its Promise
I have been reading a book recently about algorithms in the wider sense (40 algorithms every programmer should know -book and github link below)… Read More »How to approach the study of algorithms?
Venn diagram showing overlap between “quantum” and “neural network” The term “quantum neural networks” is being used with increasing frequency by the quantum computing community.… Read More »Quantum Neural Net, an Oxymoron
This article was written by Bharat Girdhar. I was always intrigued by the concept of computers taking a decision on behalf of humans. Though the… Read More »My First Neural Network
With the continuous development of network technology and the ever-expanding scale of e-commerce, the number and variety of goods grow rapidly and users need to… Read More »Building a Deep-Learning-Based Movie Recommender System
In the paper A survey on bias and fairness in machine learning.- the authors outline 23 types of bias in data for machinelearning. The source is good… Read More »23 sources of data bias for #machinelearning and #deeplearning
This article was written by Motoki Wu. Full title: How to construct prediction intervals for deep learning models using Edward and TensorFlow. Edward and Tensorflow. Source:… Read More »Adding Uncertainty to Deep Learning
The Problem Millions of people are forced to leave their current area of residence or community due to resource shortage and natural disasters such as… Read More »Using Neural Networks to Predict Cimate Change, Droughts, and Conflict Displacements
