Improving Agriculture and Sustainability with AI By Nuno Silva
Farming is a high stakes profession with many constantly changing variables. Success can be dependent on monitoring those variables that directly influence plant health. For that we need access to the right information to make good decisions. We can only manage well what we can measure. This article examines how AI can help both farmers and the environment. Read more
Boosting the power of Machine learning Model
Boosting improves the power of machine learning models to improve the accuracy of their prediction. Read more
Green Computing: The Future of Computing
The concept of Green Computing has started to gain prominence over the past few years as organizations begin to examine their carbon footprints and the impact they are having on the environment. In addition to environmental and social issues, which are becoming more important to stakeholders, the financial impacts of maintaining energy burning and heat producing IT systems make greener alternatives more attractive. Read more
What are the different skill sets required to work as a Data Analyst or a Data Scientist?
The demand for people with data skills is increasing dramatically as organizations begin to make more use of their data. As a result, salaries have increased, and more people are looking to develop a career in this sector. The question is what skills do I need to get started? Read more
Placing Restrictions on Facial Recognition Technology
The AI enabled facial recognition application once a darling of Silicon Valley as software for authenticating identities, policing and spying, is suddenly under great controversy. San Francisco, Oakland, Berkeley and Somerville, Mass., have banded all of their agencies, including the department of police, from using it. Stopping the use of such technology is become necessity for protecting civil rights Read more
Deepfakes are so named as the technology used to create fake, but extremely believable video and media representations, is based on deep learning, a branch of machine learning that applies neural net simulation to massive data sets. Read more
DataOps or DevOps? What is the difference?
Data is the key to success for many organisations and it should be gathered from and shared with all functions. This is what would happen in an ideal world. Read more
What is a Support Vector Machine?
Support Vector Machines or SVMs may sound intimidating, but they really aren’t. When I first heard about them, I became a little curious and wanted to know more. Read more
Using SAS Viya to Analyze Love at First Sight
According to the 2017 Elite Singles poll the majority of people, 61% of women and 72% of men believe in love at first sight. The paper walks you through the analysis and visualisation of a speed dating dataset garnered from Columbia University. It helps you to understand the most important factors and how to predict a potential match with that special someone, the first time we meet.
"Know thy Customer" - RFM a Smart Marketer Analysis
"Recency, Frequency, Monetary Value" or commonly known as RFM analysis is the key tool for many smart marketers who understand the importance of "Know thy Customer". Using segmentation you divide the audience into homogenous groups in order to send targeted messages.
INTELLIGENT AGENT BASED BUSINESS INTELLIGENCE
Business Intelligence (BI) Systems describes a form of data driven Decision Support Systems (DSS) that integrate a variety of concepts and technologies to gather, store and analyse data. Agent and Multi Agent System (MAS) is often mentioned as an approach to design and develop flexible and distributed software systems.
Raw Data Collection 2020: Principles and Challenges
Raw Data (also known and often referred to as Primary Data) collection is the starting point of any data analysis. Once the RD (Raw Data) is collected, it is processed to turn it into Information that can be converted into Knowledge further down the analysis track. The purpose of this White Paper is to explain the key RD collection principles and challenges and how these principles and challenges.
ARK STACK DEPLOYMENT: AN INTEROPERABILITY SOLUTION FOR SEAMLESS BLOCKCHAIN ECOSYSTEM
Ark – a decentralized mechanism invented to increase user assumption of blockchain technology. Ark plans to make a whole environment of connected affixes by giving simple to utilize devices to send our own blockchain. Being versatile and adaptable, it enables items to be procured by the across the board and make the use of the technology easier to use.
BIG DATA ANALYTICS: IDEA, DATA TYPES AND REFERENCE ARCHITECTURE
Data Analytics is the study of breaking down information to change over data to helpful information. This information could assist us with understanding our reality better, and in numerous settings empower us to settle on better choices.
Dear peopleI have recently introtuced Ocean Protocol to you all and I'm happy to say since then a Data Economy Challenge has been held. This was the first of many where participants could build on the protocol and get rewarded for it. The winners even get support to fully build out their business. Check out the submissions here and winners here:https://oceanprotocol.devpost.com/submissionsAlso there's a new forum coming up where you can contact devs and participants. https://port.oceanprotocol.com/Let me know what do you thinkREAD MORE
Data science online certification course on Geeklurn https://www.geeklurn.com/data-science-with-python/READ MORE
Keep the model simple—take fewer variables into account, thereby removing some of the noise in the training dataUse cross-validation techniques, such as k folds cross-validation Use regularization techniques, such as LASSO, that penalize certain model parameters if they're likely to cause overfittingREAD MORE
Randomly select 'k' features from a total of'm' features where k << mAmong the 'k' features, calculate the node D using the best split pointSplit the node into daughter nodes using the best splitRepeat steps two and three until leaf nodes are finalized Build forest by repeating steps one to four for 'n' times to create 'n' number of treesREAD MORE
Take the entire data set as inputCalculate entropy of the target variable, as well as the predictor attributesCalculate your information gain of all attributes (we gain information on sorting different objects from each other)Choose the attribute with the highest information gain as the root node Repeat the same procedure on every branch until the decision node of each branch is finalizedREAD MORE
Logistic regression measures the relationship between the dependent variable (our label of what we want to predict) and one or more independent variables (our features) by estimating probability using its underlying logistic function (sigmoid).READ MORE
Thanks for sharing. It is indeed a interesting read.READ MORE
The binomial distribution consists of the probabilities of each of the possible numbers of successes on N trials for independent events that each have a probability of π (the Greek letter pi) of occurring.READ MORE
Basically, an interaction is when the effect of one factor (input variable) on the dependent variable (output variable) differs among levels of another factor.READ MORE
he central limit theorem states that if you have a population with mean μ and standard deviation σ and take sufficiently large random samples from the population with replacement , then the distribution of the sample means will be approximately normally distributed..Whats your thought?READ MORE
Regularization is a process of constraining the learning of the model to reduce overfitting.READ MORE
Cross-validation.Train with more data.Remove features. ...Early stopping. ...Regularization. ...Ensembling. What are some of the other ideas?READ MORE
GOOD with numbers? Fascinated by data? The sound you hear is opportunity knocking. Mo Zhou was snappedREAD MORE
The technologies are expected to become an important part of national security, and some worry the United States is behind China in their development.READ MORE
Investment banks and hedge funds aren't alone in incorporating data science into their business models. Private equity funds are also turning to data science, both to win deals in the first place and to help them manage portfolio companies after a purchaseREAD MORE
The mysterious coronavirus is spreading at an alarming rate. There have been at least 305 deaths as more than 14,300 persons have been infected.READ MORE
When thinking about using big data, back-end processes, such as billing, cannot be ignored. Most healthcare organizations engage in a complicated receivables process involving multiple vendors. These vendors work with healthcare organizations on boutique processes ranging from payer denials to patient collections.READ MORE
By 2020 there will be over 2.7 million data scientist job openings to take on this massive growth.READ MORE
Bias and the prospect of societal harm increasingly plague artificial-intelligence research — but it’s not clear who should be on the lookout for these problems.READ MORE
AI has helped us decode ancient languages, and now researchers are turning the same technique to help understand our petsREAD MORE
With digital technologies set to irrevocably change the face of our healthcare systems, the ethical concerns surrounding the use of artificial intelligence (AI) are increasingly gaining prominence in policy circles.READ MORE
Researchers had feared that the model, known as "GPT-2", was so powerful that it could be maliciously misused by everyone from politicians to scammers.READ MORE
Until recently, artificial intelligence has struggled to gain a foothold on Wall Street. No longer.READ MORE
AI and big data represent the future of investing. Their broad application is likely to usher in perhaps the most significant change in the history of the industry. Why? Because with AI and big data: Analysts will be able to perform more thorough analysis. Portfolio managers will make better informed decisions.READ MORE
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