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Making Machine Learning More Accessible for Application Developers

Introduction

Attempts at hand-crafting algorithms for understanding human-generated content have generally been unsuccessful. For example, it is difficult for a computer to “grasp” the semantic content of an image - e.g., a car, cat, coat, etc....… - purely by analyzing its low-level pixels. Color histograms and feature detectors worked to a certain extent, but they were rarely accurate for most applications.

In the past decade, the combination of big data and deep learning has fundamentally changed the way we approach computer vision, natural language processing, and other machine learning (ML) applications; tasks ranging from spam email detection to realistic text-to-video synthesis have seen incredible strides, with accuracy metrics on specific tasks reaching superhuman levels. A significant positive side effect of these improvements is an increase in the use of embedding vectors, i.e., model artifacts generated by taking an intermediate result within a deep neural network. OpenAI’s docs page gives an excellent overview:

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ML Algorithms in Online User Reviews for Sentiment Analysis

The online ecosystem is designed to be open for live interactions. Online users can indulge in immersive web pages, engage in social conversations, and post online reviews. Web platforms are built to encourage users to post opinions without restrictions. This has stretched the scope for building several meaningful digital experiences in the web ecosystem.

Online products and services get positive and, every now and then, negative reviews. A fair amount of scrutinizing behavior by users can be spotted on most online platforms. Heaps of reviews are posted online by users on marketplaces, community web pages, and social media pages. The growing volume of user review data, especially that causes damage to a company’s online reputation, requires efficient management. This has pushed companies to monitor what users write about the products and the services across the web and adopt the methodologies of sentiment analysis.

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Using Machine Learning to Detect Dupes: Some Real-Life Examples

As companies collect more and more data about their customers, an increased amount of duplicate information starts appearing in the data as well, causing a lot of confusion among internal teams. Since it would be impossible to manually go through all of the data and delete the duplicates, companies have come up with machine learning solutions that perform such work for them. Today we would like to take a look at some interesting uses of machine learning to catch duplicates in all kinds of environments. Before we dive right in, let’s take a look at how machine learning systems work.

How Do Machine Learning Systems Identify Duplicates?

When a person looks at an image or two strings of data it would be fairly easy for them to determine whether or not the images or strings are duplicates. However, how would you train a machine to spot such duplicates? Perhaps a good starting point would be to identify all of the similarities, but then you would need to explain exactly what 'similar' means. Are there gradations to similarities? In order to overcome such challenges, researchers use string metrics to train machine learning models.

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A Beginner’s Guide to Machine Learning: What Aspiring Data Scientists Should Know

A Beginner's Guide to Machine Learning

Before choosing a machine learning algorithm, it's important to know their characteristics to generate desired outputs and build smart systems.

Data science is growing super fast. As the demand for AI-enabled solutions is increasing, delivering smarter systems for industries has become essential. And the correctness and efficiency through machine learning operations must be fulfilled to ensure the developed solutions complete all demands. Hence, applying machine learning algorithms on the given dataset to produce righteous results and train the intelligent system is one of the most essential steps from the entire process.

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Evaluation of ML Algorithms for Intrusion Detection Systems

The last decade has seen rapid advancements in machine learning techniques enabling automation and predictions in scales never imagined before. This further prompts researchers and engineers to conceive new applications for these beautiful techniques. It wasn’t long before machine learning techniques were used in reinforcing network security systems.

The most common risk to a network’s security is an intrusion such as brute force, denial of service, or even an infiltration from within a network. With the changing patterns in network behavior, it is necessary to switch to a dynamic approach to detect and prevent such intrusions. A lot of research has been devoted to this field, and there is a universal acceptance that static datasets do not capture traffic compositions and interventions.