Three datasets are intended to be used for exploring machine learning applications in materials science. They are formatted in simple form and in particular for easy input into the MAterials Simulation Toolkit - Machine Learning (MAST-ML) package (see https://github.com/uw-cmg/MAST-ML).Each dataset is a materials property of interest and associated descriptors. For detailed information, please see the attached REAME text file.The first dataset for dilute solute diffusion can be used to predict an effective diffusion barrier for a solute element moving through another host element. The dataset has been calculated with DFT methods.The second dataset for perovskite stability gives energies of compostions of potential perovskite materials relative to the convex hull calculated with DFT. The perovskite dataset also includes columns with information about the A site, B site, and X site in the perovskite structure in order to perform more advanced grouping of the data.The third dataset is a metallic glasses dataset which has values of reduced glass transition temperature (Trg) for a variety of metallic alloys. An additional column is included for majority element for each alloy, which can be an interesting property to group on during tests.

Source

This dataset is a modified version of the California Housing dataset available from Luís Torgo's page (University of Porto). Luís Torgo obtained it from the StatLib repository (which is closed now). The dataset may also be downloaded from StatLib mirrors.

This dataset appeared in a 1997 paper titled Sparse Spatial Autoregressions by Pace, R. Kelley and Ronald Barry, published in the Statistics and Probability Letters journal. They built it using the 1990 California census data. It contains one row per census block group. A block group is the smallest geographical unit for which the U.S. Census Bureau publishes sample data (a block group typically has a population of 600 to 3,000 people)

Tweaks

The dataset in this directory is almost identical to the original, with two differences:

207 values were randomly removed from the total_bedrooms column, so we can discuss what to do with missing data. An additional categorical attribute called ocean_proximity was added, indicating (very roughly) whether each block group is near the ocean, near the Bay area, inland or on an island. This allows discussing what to do with categorical data. Note that the block groups are called "districts" in the Jupyter notebooks, simply because in some contexts the name "block group" was confusing.

Context

This is the dataset used in the section "ANN (Artificial Neural Networks)" of the Udemy course from Kirill Eremenko (Data Scientist & Forex Systems Expert) and Hadelin de Ponteves (Data Scientist), called Deep Learning A-Z™: Hands-On Artificial Neural Networks. The dataset is very useful for beginners of Machine Learning, and a simple playground where to compare several techniques/skills.

It can be freely downloaded here: https://www.superdatascience.com/deep-learning/

The story: A bank is investigating a very high rate of customer leaving the bank. Here is a 10.000 records dataset to investigate and predict which of the customers are more likely to leave the bank soon.

The story of the story: I'd like to compare several techniques (better if not alone, and with the experience of several Kaggle users) to improve my basic knowledge on Machine Learning.

Content

I will write more later, but the columns names are very self-explaining.

Acknowledgements

Udemy instructors Kirill Eremenko (Data Scientist & Forex Systems Expert) and Hadelin de Ponteves (Data Scientist), and their efforts to provide this dataset to their students.

Inspiration

Which methods score best with this dataset? Which are fastest (or, executable in a decent time)? Which are the basic steps with such a simple dataset, very useful to beginners?

Emotion Prediction with Quantum5 Neural Network AI Machine Learning - By Emirhan BULUT

V1

I have created an artificial intelligence software that can make an emotion prediction based on the text you have written using the Semi Supervised Learning method and the RC algorithm. I used very simple codes and it was a software that focused on solving the problem. I aim to create the 2nd version of the software using RNN (Recurrent Neural Network). I hope I was able to create an example for you to use in your thesis and projects.

V2

I decided to apply a technique that I had developed in the emotion dataset that I had used Semi-Supervised learning in Machine Learning methods before. This technique is produced according to Quantum5 laws. I developed a smart artificial intelligence software that can predict emotion with Quantum5 neuronal networks. I share this software with all humanity as open source on Kaggle. It is my first open source project in NLP system with Quantum technology. Developing the NLP system with Quantum technology is very exciting!

Happy learning!

Emirhan BULUT

Head of AI and AI Inventor

Emirhan BULUT. (2022). Emotion Prediction with Quantum5 Neural Network AI [Data set]. Kaggle. https://doi.org/10.34740/KAGGLE/DS/2129637

The coding language used:

Python 3.9.8

Libraries Used:

Keras

Tensorflow

NumPy

Pandas

Scikit-learn (SKLEARN)

https://raw.githubusercontent.com/emirhanai/Emotion-Prediction-with-Semi-Supervised-Learning-of-Machine-Learning-Software-with-RC-Algorithm---By/main/Quantum%205.png" alt="Emotion Prediction with Quantum5 Neural Network on AI - Emirhan BULUT">

https://raw.githubusercontent.com/emirhanai/Emotion-Prediction-with-Semi-Supervised-Learning-of-Machine-Learning-Software-with-RC-Algorithm---By/main/Emotion%20Prediction%20with%20Semi%20Supervised%20Learning%20of%20Machine%20Learning%20Software%20with%20RC%20Algorithm%20-%20By%20Emirhan%20BULUT.png" alt="Emotion Prediction with Semi Supervised Learning of Machine Learning Software with RC Algorithm - Emirhan BULUT">

Developer Information:

Name-Surname: Emirhan BULUT

Contact (Email) : emirhan@isap.solutions

LinkedIn : https://www.linkedin.com/in/artificialintelligencebulut/

Kaggle: https://www.kaggle.com/emirhanai

Official Website: https://www.emirhanbulut.com.tr

Machine learning (ML) has gained much attention and has been incorporated into our daily lives. While there are numerous publicly available ML projects on open source platforms such as GitHub, there have been limited attempts in filtering those projects to curate ML projects of high quality. The limited availability of such high-quality dataset poses an obstacle to understanding ML projects. To help clear this obstacle, we present NICHE, a manually labelled dataset consisting of 572 ML projects. Based on evidences of good software engineering practices, we label 441 of these projects as engineered and 131 as non-engineered. In this repository we provide "NICHE.csv" file that contains the list of the project names along with their labels, descriptive information for every dimension, and several basic statistics, such as the number of stars and commits. This dataset can help researchers understand the practices that are followed in high-quality ML projects. It can also be used as a benchmark for classifiers designed to identify engineered ML projects.

GitHub page: https://github.com/soarsmu/NICHE

This is a dataset prepared and intended as a data source for development of stress analysis methods based on machine learning. The dataset is based on finite element (FEM/FEA) stress analyses of generated mechanical structures using PyCalculix Python API. The dataset contains more than 270,794 pairs of stress analyses images (von Mises stress) of randomly generated 2D structures with predefined thickness and material properties. All the structures are fixed at their bottom edges and loaded with gravity force only. See PREVIEW directory with some examples.

The UCI Machine Learning Repository is a collection of databases, domain theories, and data generators that are used by the machine learning community for the empirical analysis of machine learning algorithms. The archive was created as an ftp archive in 1987 by David Aha and fellow graduate students at UC Irvine. Since that time, it has been widely used by students, educators, and researchers all over the world as a primary source of machine learning data sets. As an indication of the impact of the archive, it has been cited over 1000 times, making it one of the top 100 most cited "papers" in all of computer science. The current version of the web site was designed in 2007 by Arthur Asuncion and David Newman, and this project is in collaboration with Rexa.info at the University of Massachusetts Amherst. Funding support from the National Science Foundation is gratefully acknowledged. Many people deserve thanks for making the repository a success. Foremost among them are the d

UCI Machine Learning Repository is a collection of over 550 datasets.

The application of machine learning has rapidly evolved in medicine over the past decade. In stroke, commercially available machine learning algorithms have already been incorporated into clinical application for rapid diagnosis. The creation and advancement of deep learning techniques have greatly improved clinical utilization of machine learning tools and new algorithms continue to emerge with improved accuracy in stroke diagnosis and outcome prediction. Although imaging-based feature recognition and segmentation have significantly facilitated rapid stroke diagnosis and triaging, stroke prognostication is dependent on a multitude of patient specific as well as clinical factors and hence accurate outcome prediction remains challenging. Despite its vital role in stroke diagnosis and prognostication, it is important to recognize that machine learning output is only as good as the input data and the appropriateness of algorithm applied to any specific data set. Additionally, many studies on machine learning tend to be limited by small sample size and hence concerted efforts to collate data could improve evaluation of future machine learning tools in stroke. In the present state, machine learning technology serves as a helpful and efficient tool for rapid clinical decision making while oversight from clinical experts is still required to address specific aspects not accounted for in an automated algorithm. This article provides an overview of machine learning technology and a tabulated review of pertinent machine learning studies related to stroke diagnosis and outcome prediction.

The goal of this dataset is to better undertand how open source machine learning projects evolve. Data collection date: early May 2018. Source: GitHub user interface and API. Contains original research.

Presentation

Columns

• name - name of the project.
• alignment - either corporate, academia or indie. Corporate projects are being developed by professional engineers, typically have a dedicated development team and trying to solve specific problems. Academical projects usually mention publications, they help to research. Independent projects are often a hobby.
• company - name of the company if the alignment is corporate.
• forecast - expected middle-term evolution of the project. 1 means positive, 0 means negative (stagnation) and -1 means factual death.
• year - when the project was created. Defaults to the GitHub repository creation date but can be earlier - this is a subject of manual adjustments.
• code of conduct - whether the project has a code of conduct.
• contributing - whether the project has a contributions guide.
• stars - number of stargazers on GitHub.
• issues - number of issues on GitHub, either open or closed.
• contributors - number of contributors as reported by GitHub.
• core - estimation of the core team aka "bus factor".
• team - number of people which commit to a project regularly.
• commits - number of commits in the project.
• team / all - ratio of the number of commits by the dedicated development team to the overall number of contributions. Indicates roughly which part of the project is own by the internal developers.
• link - URL of the project.
• language - API language. multi means several languages.
• implementation - the language which was mainly used for implementing the project.
• license - license of the project.

Contributing

Feel free to correct any mistakes or append other open machine learning projects.

With recent success in supervised learning, artificial intelligence (AI) and machine learning (ML) can play a vital role in precision medicine. Deep learning neural networks have been used in drug discovery when larger data is available. However, applications of machine learning in clinical trials with small sample size (around a few hundreds) are limited. We propose a Similarity-Principle-Based Machine Learning (SBML) method, which is applicable for small and large sample size problems. In SBML, the attribute-scaling factors are introduced to objectively determine the relative importance of each attribute (predictor). The gradient method is used in learning (training), that is, updating the attribute-scaling factors. We evaluate SBML when the sample size is small and investigate the effects of tuning parameters. Simulations show that SBML achieves better predictions in terms of mean squared errors for various complicated nonlinear situations than full linear models, optimal and ridge regressions, mixed effect models, support vector machine and decision tree methods.

Energy consumption predictions for buildings play an important role in energy efficiency and sustainability research. Accurate energy predictions have numerous application in real-time performance monitoring, fault detection, identifying prime targets for energy conservation, quantifying savings resulting from energy efficiency projects, etc. Machine learning-based energy models have proved to be more efficient and accurate where historical time series data is available. This paper presents various machine learning concepts that will aid in the generation of more accurate and efficient energy models. We have shown in detail the development of energy models using extreme gradient boosting (XGBoost), artificial neural network (ANN), and degree-day-based ordinary least square regression. We have presented a thorough description of the workflow, including intermediate steps for feature engineering, feature selection, hyper-parameter optimization and the Python source code. Our results indicate that XGBoost produces highly accurate energy models, and the intermediate steps are particularly important for XGBoost and ANN model development.

We have an in-house team of Data Scientists & Data Engineers along with sophisticated data labeling, data pre-processing, and data wrangling tools to speed up the process of data management and ML model development. We have an AI-enabled platform "ADVIT", the most advanced Deep Learning (DL) platform to create, manage high-quality training data and DL models all in one place. ADVIT simplifies the working of your DL Application development.

Introducing Salutary Data's comprehensive B2B Data Lake solution, a game-changing resource optimized for companies looking to revolutionize their data management and utilization processes.

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In today's data-driven world, the right Data Lake is your gateway to success. Choose Salutary Data for comprehensive B2B Data Lake solutions that unleash the full potential of your data, integrate seamlessly with AI and ML tools, and enable the creation of custom data processes that set you apart from the competition.

The machine learning market size was estimated at USD 38.11 billion in 2022 and is projected to surpass around USD 771.38 billion by 2032 with a CAGR of 35.09%.

The v1 dataset includes AIA/HMI observations 2010-2018 and v2 includes AIA/HMI observations 2010-2020 in all 10 wavebands (94A, 131A, 171A, 193A, 211A, 304A, 335A, 1600A, 1700A, 4500A), with 512x512 resolution and 6 minutes cadence; HMI vector magnetic field observations in Bx, By, and Bz components, with 512x512 resolution and 12 minutes cadence; The EVE observations in 39 wavelengths from 2010-05-01 to 2014-05-26, with 10 seconds cadence.

Snapshot img

US Deep Learning Market Size 2023-2027

The US Deep Learning Market size is estimated to increase by USD 3.31 billion and grow at a CAGR of 29.19% between 2022 and 2027. The growth of the market depends on several factors, including industry-specific solutions, increased focus on neuroscience-based deep learning and increasing entry of startups. Deep learning is a subfield of artificial intelligence (AI) and machine learning that focuses on the development and training of neural networks, particularly deep neural networks, to perform tasks that traditionally require human intelligence. Deep learning has a wide range of applications, including computer vision (e.g., object detection and image segmentation), natural language processing (e.g., machine translation and sentiment analysis), speech recognition, recommendation systems, autonomous vehicles, and more.

What will be the size of the Market During the Forecast Period?

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Market Segmentation

This market report extensively covers market segmentation by application (image recognition, voice recognition, video surveillance and diagnostics, and data mining), type (software, services, and hardware), and end-user (security, automotive, healthcare, retail and commerce, and others). It also includes an in-depth analysis of drivers, trends, and challenges. Furthermore, the report includes historic market data from 2017 to 2021.

By Application Segment

The market share growth by image recognition segment will be significant during the forecast period. Image recognition, a subset of computer vision, involves the use of artificial intelligence (AI) and machine learning algorithms to analyze and interpret visual data from images and videos. Image recognition is used in applications like visual search, product recommendations, and inventory management. End-users can take photographs of products to find similar items, making online shopping more convenient.

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The image recognition segment was the largest and was valued at USD 244.06 million in 2017. In the automotive industry, image recognition is essential in advanced driver assistance systems (ADAS) and autonomous vehicles, as it helps in identifying pedestrians, other vehicles, road signs, and lane markings. Deep learning, particularly convolutional neural networks (CNNs), has proven to be exceptionally effective at solving image recognition and computer vision problems. The growing demand for image recognition solutions across different industries leads to increased investments in deep learning research and development, fostering innovation and the creation of specialized solutions, which will boost the growth of the deep learning market in US during the forecast period.

By Type Segment

Deep learning software refers to the category of computer programs and frameworks that are designed to facilitate the development, training, and deployment of deep neural networks for artificial intelligence (AI) and machine learning tasks. The rising demand for deep learning software has led to a competitive landscape with numerous software providers, open-source frameworks, and cloud-based AI platforms offering deep learning solutions. This competition drives further innovation and accessibility, making it easier for organizations to integrate deep learning solutions into their operations and products, which will have a positive impact on the growth of the deep learning market in US during the forecast period.

Market Dynamics and Customer Landscape

In the realm of artificial intelligence (AI) and machine learning, the United States is witnessing a profound shift propelled by several pivotal factors. The landscape is shaped by the declining hardware cost, enabling broader accessibility and adoption of cutting-edge technologies like transformers and sophisticated deep neural network architectures. As infrastructure and storage costs decrease, the scalability of AI solutions becomes more feasible, fostering the proliferation of connected devices and enhancing the capabilities of automation. This revolution extends to diverse applications, including analyzing human behavior and processing human brain cells-generated information across various formats like photos, text, and audio. The evolution is characterized by efficient classification tasks and enhanced performance through advanced techniques such as recurrent neural networks (RNNs). Amidst this transformation, a focus on security and operational costs remains paramount, especially in sectors like education institutes, where AI is revolutionizing data analysis and driving innovation.

Key Market Driver

Industry-specific solutions are notably driving market growth. Deep learning has been instrumental in developing industry-specific solutions across various end-user sectors. Its

This dataset consists of imagery, imagery footprints, associated ice seal detections and homography files associated with the KAMERA Test Flights conducted in 2019. This dataset was subset to include relevant data for detection algorithm development. This dataset is limited to data collected during flights 4, 5, 6 and 7 from our 2019 surveys.

The dataset is prepared and intended as a data source for development of a stress analysis method based on machine learning. It consists of finite element stress analyses of randomly generated mechanical structures. The dataset contains more than 270,794 pairs of stress analyses images (von Mises stress) of randomly generated 2D structures with predefined thickness and material properties. All the structures are fixed at their bottom edges and loaded with gravity force only. See PREVIEW directory with some examples. The zip file contains all the files in the dataset.