Outcome based processes — an intelligent alternative?

A good starting point to explore outcome based processes would be to review the limitations of a linear process model. A linear process expects all data objects to be treated equally and all tasks to be treated in the same order. These constraints arise from its assembly line origins, discussed in the previous article, ‘Why companies use linear process flows’.

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Bias in AI systems and efforts to fix it

Understand how racial and gender biases in artificial intelligence systems occur and how to address them. In April 2019, New York University’s AI Now Institute released a report on the impact of bias in artificial intelligence systems. About 12 months later, you might remember the Twitter the storm caused when a pixelated image of Barack Obama was turned into a higher resolution image of the former president by an AI model. Except…

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How to get the most out of Machine Learning Models

To enhance the model accuracy and avoid the regular split of the data disadvantages, we need to add more generalization to the split process. In this strategy, we are repeating the train_test_split multiple times randomly. For each split or fold, the accuracy is calculated then the algorithm aggregate the accuracies calculated from each split and averages them.

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Interoperability is essential for AI development

Interoperability in AI

Without interoperability, AI development opportunities will be limited to Big Tech, as they have access to the most data. Big Tech companies’ lock in models are affecting the development of new technologies such as Artificial intelligence (AI). Smaller companies are limited and unable to compete with Big Tech’s data stores. Developers are also limited, tied to certain services and providers, like AWS, when the best options across the AI architecture may be produced by different companies.

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FB Prophet is not always the most suitable time series forecasting model

I have written a series of posts about a univariate dataset of shampoo sales dating back from 1901 to 1903. This series of posts would not be complete, however, without a post on Facebook Prophet, an open source time series forecasting model. The finale of this series of posts, therefore, attempts to shed light on the forecasting of the shampoo dataset using Facebook Prophet, which is based on based on decomposable (trend+seasonality+holidays) models.

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Monitoring Machine Learning Models in production: How to track data quality and integrity?

Track data and integrity

How to Track Data Quality and Integrity. As the saying goes: garbage in is garbage out. Input data quality is the most crucial component of a machine learning system. Whether or not you have an immediate feedback loop, your model monitoring always starts here. There are two types of data issues one encounters. Put simply…

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Peeking into AI’s ‘black box’ brain — with physics

Cats aren’t dogs. Even modern AI knows that. But how exactly AI distinguishes cat images from those of dogs is not clear. Standard neural networks are akin to a black box, as even the people who program them often have little to no idea how they make decisions. It’s not as critical when it’s just a picture of a cute puppy or a kitten. But it becomes important when an AI tries to interpret, say, a sequence of weather images that show…

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Using Linear Regression to predict the Dow Jones Industrial Average Index

The stock market is filled with individuals who know the price of everything, but the value of nothing.” — Fischer“A prediction about the direction of the stock market tells you nothing about where stocks are headed, but a whole lot about the person doing the predicting.”

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And this, in a nutshell’s nutshell, is “Machine Learning”

Combining Part 1 and 2: Probably imagining a robot or terminator when asking about machine learning, in reality, machine learning is beyond and involved in almost all application you can imagine in our today world. Think of spam filter in your email, voice, face and fingerprint recognition on your phone, your Siri on iPhone, google assistance on android device etcetera have an iota of machine learning.

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Google trained a trillion-parameter AI language model

Parameters are the key to machine learning algorithms. They’re the part of the model that’s learned from historical training data. Generally speaking, in the language domain, the correlation between the number of parameters and sophistication has held up remarkably well. For example, OpenAI’s GPT-3 — one of the largest language models ever trained, at 175 billion parameters — can make primitive analogies, generate recipes, and even complete basic code.

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What is a Time Series GAN?

This post was originally published by Sejuti Das at Analytics India Magazine Identifying anomalies in time series data can be daunting, thanks to the vague definition of anomalies, lack of labelled data, and highly complex…

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All Machine Learning Algorithms you should know in 2021

Many machine learning algorithms exits that range from simple to complex in their approach, and together provide a powerful library of tools for analyzing and predicting patterns from data. If you are learning for the first time or reviewing techniques, then these intuitive explanations of the most popular machine learning models will help you kick off the new year with confidence.

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Five trends to look for in governing data, in 2021, for digital-driven business outcomes

To date, the organizations have focused on formalizing data consumption practices through distribution technology, access-based delivery mechanisms for analytics, and AI functions. However, with data protection laws and positive awareness across the world, firms have extended the formalization to data collection management. This in-fact is the first life-cycle stage of data.

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Top 12 Data Structure Algorithms to implement in practical applications in 2021

The new year is coming and this new year we encourage you to check out the practical scenarios of famous algorithms instead of learning them just for the sake of a job. In this blog, we will discuss some practical implementations of these algorithms in the real world. 
No matter if you’re a fresher or an experienced person, you will find it interesting to read. This article will refresh the memories of experienced programmers.  

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