(testing signal)

Tag: datasets

Cyberattacks Detection in IoT-based Smart City Network Traffic

Machine LearningIn this article, different machine learning and deep learning models have been used for the classification of cyberattacks such as DoS, Worms, Backdoor, and many more attacks from normal network traffic and network intrusion detection. UNSW-NB15 Dataset has been used to train the ML and DL models. You can find the complete code, trained models, plots, datasets, preprocessed files here on my GitHub account.Made using Draw.ioThe whole idea of the Internet of Things is to extend the capability of the Internet beyond computers and smartphones to electronic, mechanical…

Split Your Dataset With scikit-learn’s train_test_split()

Machine LearningPhoto by Isaac Smith on UnsplashModel evaluation and validation are important parts of supervised machine learning. It aids in the selection of the best model to represent our data as well as the prediction of how well that model will perform in the future.To predict this model we need to split this model dataset into training and testing data. Manually splitting out this data is difficult because of the large size of datasets and data needs to be shuffled.For making this task easier we will use Scikit-learn’s train_test_split() module, which will split our data into…

A Guide to Machine Learning Pipelines and Orchest

Machine LearningLearn how machine learning pipelines are used in productions and design your first pipeline using simple steps on disaster tweets classification datasets. You will also learn how to ingest the data, preprocess, train, and eventually evaluate the results.Image 1IntroductionIn this guide, we will learn the importance of Machine Learning (ML) pipelines and how to install and use the Orchest platform. We will be also using Natural Language Processing beginner problem from Kaggle by classifying tweets into disaster and non-disaster tweets. The ML pipelines are independently…

How Image Annotation is Leading the Way in ML and AI

Without advanced image annotation techniques, it is not possible to overcome the hurdle of preparing an AI-based automated system to tag images or recognize objects of interest.

Artificial Intelligence is like a child superhero. It’s got impeccable memory, unimaginable capabilities, and limitless endurance. However, it doesn’t know what to do or when to do it. It needs to be told. Data annotations, or image annotations in the case of computer-vision AI, hold the key to this communication. Without high-quality datasets and ML training, image-based decision-making by AI runs the…

IMDEX bolsters real-time rock knowledge with Datarock investment


Posted by Daniel Gleeson on 15th November 2021

IMDEX says it has boosted its rock knowledge capabilities with a deal to acquire an initial 30% stake in image analysis company Datarock for A$5.5 million ($4 million).
Datarock has, IMDEX says, extensive geoscience and data science expertise that has led to the development of a cloud-based platform which applies artificial intelligence and machine learning to automate the extraction of geological and geotechnical information from core imagery, videos, and point clouds. This automation creates high value datasets that drive…

Improving Signal Classification using Visual AI

“One look is worth a Thousand Words” This phrase was used in 1913 to convey that graphics had a place in newspaper publishing. More than a hundred years later, this phrase still rings true, especially for data scientists. In this post, we show how converting data to images can provide greater accuracy for signal classification problems by leveraging multi-modal datasets instead of plain tabular,structured datasets. While this may sound complicated, using DataRobot makes this much easier.

Signal classification models are typically built using time series principles; traditionally…

Six warnings You Ignore That Might Put Image Classification Dataset at risk

Deep Learning“Opportunity never knocks twice,” as the saying goes, but in the hands of image annotators, this clear-cut leaflet will assist the data scientists in addressing gaps in the training datasets that were left neglected or disregarded throughout the image cleaning process.The sole obligation of an image annotator working on an image classification assignment is not just to complete the picture labelling task at hand. But also to tell data scientists about the following alarms,…

A PySpark Example for Dealing with Larger than Memory Datasets

A step-by-step tutorial on how to use Spark to perform exploratory data analysis on larger than memory datasets.Analyzing datasets that are larger than the available RAM memory using Jupyter notebooks and Pandas Data Frames is a challenging issue. This problem has already been addressed (for instance here or here) but my objective here is a little different. I will be presenting a method for performing exploratory analysis on a large data set with the purpose of identifying and filtering out…

Sometimes Bigger Machine Learning Models and Larger Datasets Can Hurt Performance

OpenAI Double Descent Hypothesis research shows a phenomenon that challenges both traditional statistical learning theory and conventional wisdom in machine learning practitioners.Source: https://www.youtube.com/watch?v=Kih-VPHL3gAI recently started an AI-focused educational newsletter, that already has over 100,000 subscribers. TheSequence is a no-BS (meaning no hype, no news etc) ML-oriented newsletter that takes 5 minutes to read. The goal is to keep you up to date with machine learning…

https://pub.towardsai.net/sometimes-bigger-machine-learning-models-and-larger-datasets-can-hurt-performance-ae26ab530e67?source=rss—-98111c9905da—4

3. Real-World Applications Of Machine Learning In Healthcare

Real-World Applications of Machine Learning
Disease Detection & Efficient Diagnosis

One of the major use cases of machine learning in healthcare lies in the early detection and efficient diagnosis of diseases. Concerns such as hereditary and genetic disorders and certain types of cancers are hard to identify in the early stages but with well-trained machine learning solutions, they can be precisely detected.

Such models undergo years of training from computer vision and other datasets. They are trained to spot even the slightest of anomalies in the human body or an organ to trigger a notification for further analysis. A good example of this use case is IBM Watson Genomic, whose genome-driven sequencing model powered by cognitive computing allows for faster and more effective ways to diagnose concerns.… Read more...

Better Quantifying the Performance of Object Detection in Video

source

Is Unstructured Data the Future of Data Management?

Click to learn more about author Nahla Davies.

In an increasingly tech-reliant world, data informs and powers much of our day-to-day lives. Data can be used to enhance AI capabilities, create personalized experiences, or be applied in medical research to help save lives. However, the biggest question remains: What is the best method to store, organize, and use the vast amounts of data at our disposal?

Enter unstructured data management. Organizations are increasingly looking to unstructured data for analytic, regulatory, and decision-making processes. From business intelligence to marketing campaigns, it’s not uncommon for unstructured data analysis to drive human decision-making. So, let’s take a look at unstructured data management to answer the question “Is unstructured data management the future of data analytics?”… Read more...

Real-World Applications Of Machine Learning In Healthcare

Real-World Applications of Machine Learning

Disease Detection & Efficient Diagnosis

One of the major use cases of machine learning in healthcare lies in the early detection and efficient diagnosis of diseases. Concerns such as hereditary and genetic disorders and certain types of cancers are hard to identify in the early stages but with well-trained machine learning solutions, they can be precisely detected.

Such models undergo years of training from computer vision and other datasets. They are trained to spot even the slightest of anomalies in the human body or an organ to trigger a notification for further analysis. A good example of this use case is IBM Watson Genomic, whose genome-driven sequencing model powered by cognitive computing allows for faster and more effective ways to diagnose concerns.

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Tips For Data Mapping And Replacing With Pandas And Numpy

In order to summarize main characteristics, spot anomalies, and visualize information, you should know how to rearrange and transform datasets. In other words, transforming data helps you play with your dataset, make sense of it, and gather as many insights as you can. In this article, I will show you some of my commonly used methods to play with data, and hope this would be helpful.

I will create a simple score dataset, which includes information about different classes’ grades.

Input: 
info = {'Class':['A1', 'A2', 'A3', 'A4','A5'],
'AverageScore':[3.2, 3.3, 2.1, 2.9, 'three']}
data = pd.DataFrame(info)

Output:

Fig 1: DataFrame

As the Average Score of Class A5 in our data is a string object, I want to replace it with a corresponding number for easier data manipulation.

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Custom datasets in Pytorch — Part 2. Text (Machine Translation)

Custom datasets in Pytorch — Part 2. Text (Machine Translation)
Photo by Pickawood on Unsplash
Figure 1. Dataset (source: Image by author)

1. Import Libraries

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The Mystery of Feature Scaling is Finally Solved

Photo by Danist Soh on Unsplash
Dave Guggenheim

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Geospatial Data File Format Conversions (KML, SHP, GeoJSON)

Use these JavaScript utilities. No cost. No installation. No quota. (HTML File included)

Screenshot by Author | The typical popup window I face when I attempt to convert a spatial data file with online utilities
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Messy Data is Beautiful

 

Once these types of data have been cleaned, they do more than show organized data sets. They reveal unlimited possibilities, and AI analytics can reveal these possibilities faster and more efficiently than ever before.

Sponsored Post.
 

Data scientists have always been expected to curate data into ‘aha’ moments and tell stories that can reach a wider business audience. But what is the cost of this curation?

The real signal is in the noise

Tidy data doesn’t help that much.

Every aggregation and pivot performed on datasets reduces the total amount of information available to analyze. That clever NLP topic mining on free text fields was no doubt very useful, but the raw text is more interesting. Perhaps those ‘meaningless’ raw sensor logs are just that, or not.

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Simple Image Classification Using FastAI.jl

The Fastai library is now on Julia with similar features available in Python. In this project, we are going to train the Resnet-18 model to classify images from the ImageNet dataset in few steps.

Image by Author | Elements by freepik

The FastAI.jl library is similar to the fast.ai library in Python and it’s the best way to experiment with your deep learning projects in Julia. The library allows you to use state-of-the-art models that you can modify, train, and evaluate by using few lines of code. The FastAI.jl provides a complete ecosystem for deep learning which includes computer vision, Natural Language processing, tabular data, and more submodules are added every month FastAI (fluxml.ai).

In this project, we are going to use the fastai library to train an image classifier on the Imagenette dataset.

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Exploring DataCommons — the API powering Google Search

A new paradigm for querying public datasets

Image by author
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A Look Into Global, Cohort and Local Model Explainability

A primer on the different levels of explainability and how each can be used across the ML lifecycle

In the last decade, significant technological progress has been driven rapidly by numerous advances in applications of machine learning. Novel ML techniques have revolutionized industries by cracking historically elusive problems in computer vision, natural language processing, robotics, and many others. Today it’s not hyperbolic to say that ML has changed how we work, how we shop, and how we play.

While many models have increased in performance, delivering state-of-the-art results on popular datasets and challenges, models have also increased in complexity. In particular, the ability to introspect and understand why a model made a particular prediction has become more and more difficult.… Read more...