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3 Types of Preventative Maintenance for Data Centers
/0 Comments/in Predictive Analytics /by Shannon FlynnImage Source: source unsplash.com Downtime for a data center can be extraordinarily costly — potentially leading to lost revenue, lost customers and a damaged reputation. Preventative maintenance (PM) helps keep essential data center equipment running for as long as possible (while also making potential issues easier to spot). However, there are many strategies for preventative […]
It’s All About Data: The Training of AI Models
/0 Comments/in Artificial Intelligence, Data Science, Deep Learning, Machine Learning, Main Category /by Benjamin AunkoferIn deep learning, there are different training methods. Which one we use in an AI project depends on the data provided by our customer: how much data is there, is it labeled or unlabeled? Or is there both labeled and unlabeled data?
Automatic Financial Trading Agent for Low-risk Portfolio Management using Deep Reinforcement Learning
/0 Comments/in Artificial Intelligence, Data Science, Deep Learning, Machine Learning, Main Category /by Jieyun HuThis article focuses on autonomous trading agent to solve the capital market portfolio management problem. Researchers aim to achieve higher portfolio return while preferring lower-risk actions. It uses deep reinforcement learning Deep Q-Network (DQN) to train the agent.
Process Mining Camp 2022
/0 Comments/in Events /by Anne Rozinat & Christian W. GüntherPack your bags, get your provisions, and plan your trip — Just a few more weeks until we get together at this year’s Process Mining Camp on Thursday, 23 June in Eindhoven, the Netherlands.
How Do Various Actor-Critic Based Deep Reinforcement Learning Algorithms Perform on Stock Trading?
/0 Comments/in Artificial Intelligence, Deep Learning, Machine Learning, Main Category, Use Case, Use Cases /by Zhuotian TangDeep Reinforcement Learning (DRL) is a blooming field famous for addressing a wide scope of complex decision-making tasks. This article would introduce and summarize the paper “Deep Reinforcement Learning for Automated Stock Trading.
Generative Adversarial Networks
/0 Comments/in Artificial Intelligence, Data Mining, Deep Learning, Machine Learning, Main Category /by Sunil YadavAfter Deep Autoregressive Models, Deep Generative Modelling and Variational Autoencoders we now continue the discussion with Generative Adversarial Networks (GANs).
CCNA vs. CCNP vs. CCIE Security Certification
/0 Comments/in Certification / Training, Data Security, Education / Certification /by Shannon FlynnLearn more about Cisco certifications and learn the difference between CCNA, CCNP and CCIE certifications to help you choose which path is right for you.
How to maintain product quality with deep learning
/0 Comments/in Artificial Intelligence, Data Science, Deep Learning, Machine Learning, Main Category, Use Case, Use Cases /by Benjamin AunkoferDeep Learning helps companies to automate operative processes in many areas. Industrial companies in particular also benefit from product quality assurance. Computer Vision enables automation to identify scratches and cracks on product item surfaces.
Understanding Linear Regression with all Statistical Terms
/0 Comments/in Data Science, Main Category /by M.Nithya LakshmiLinear Regression Model – This article is about understanding the linear regression with all the statistical terms. What is Regression Analysis? regression is an attempt to determine the relationship between one dependent and a series of other independent variables. Regression analysis is a form of predictive modelling technique which investigates the relationship between a dependent […]
Variational Autoencoders
/0 Comments/in Artificial Intelligence, Data Science, Deep Learning, Machine Learning, Main Category, Use Cases /by Sunil Yadavariational autoencoders (VAEs) are a deep learning method to produce synthetic data (images, texts) by learning the latent representations of the training data. AGMs are sequential models and generate data based on previous data points by defining tractable conditionals.