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.
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
Generative Adversarial Networks (GANs) software is software for producing forgeries and imitations of data (aka synthetic data, fake data). Human beings have been making fakes,… Read More »Generative Adversarial Networks (GANs) & Bayesian Networks
View of Mount Vesuvius form Pompeii In the last week, I started a new book on classical (not quantum) Bayesian Networks. Today, I uploaded the… Read More »Today I uploaded to github the first version of my book "Bayesuvius" about Bayesian Networks
The power of deep learning paired with collaborative human intelligence to increase the quality of crop cultivation imagery through super resolution. The Problem The focus in… Read More »Enhancing Satellite Imagery Through Super-Resolution
Abstract Implementing some of the pillars of an automated machine learning pipeline such as (i) Automated data preparation, (ii) Feature engineering, (iii) Model building in… Read More »DriveML: Self-Drive Machine Learning Projects
This article was written by Joyce Xu. With the rise of autonomous vehicles, smart video surveillance, facial detection and various people counting applications, fast and… Read More »Deep Learning for Object Detection: A Comprehensive Review
This article was written by Madhu Sanjeevi (Mady). In the previous story we talked about Linear Regression for solving regression problems in machine learning, This… Read More »Logistic Regression with Math
