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Movements, leaders, victories and the continuing fight for equality.
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Explore the people, places, events, achievements, struggles and stories that shaped our journey.
Movements, leaders, victories and the continuing fight for equality.
Innovation, patents, science, technology and world-changing contributions.
Pioneers, champions, Negro Leagues, records, activism and excellence.
Meet the people whose lives, choices and achievements shaped the journey.
Black towns, communities, institutions and places where history happened.
Moments that changed communities, movements, institutions and the nation.
In 1968 Shirley Chisholm became the first Black woman elected to the United States Congress. In 1972 she launched a campaign for the Democratic presidential nomination, breaking another political barrier.
MORE →Reflects the personal views, recollections, and perspective of the author, Mike Davis.
This is a personal recollection on the Move fire on May 13, 1985
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Automated machine learning (AutoML) is the process of automating the tasks of applying machine learning to real-world problems. It is the combination of automation and ML.[1]
AutoML potentially includes every stage from beginning with a raw dataset to building a machine learning model ready for deployment. AutoML was proposed as an artificial intelligence-based solution to the growing challenge of applying machine learning.[2][3] The high degree of automation in AutoML aims to allow non-experts to make use of machine learning models and techniques without requiring them to become experts in machine learning. Automating the process of applying machine learning end-to-end additionally offers the advantages of producing simpler solutions, faster creation of those solutions, and models that often outperform hand-designed models.[4]
Common techniques used in AutoML include hyperparameter optimization, meta-learning and neural architecture search.
In a typical machine learning application, practitioners have a set of input data points to be used for training. The raw data may not be in a form that all algorithms can be applied to. To make the data amenable for machine learning, an expert may have to apply appropriate data pre-processing, feature engineering, feature extraction, and feature selection methods. After these steps, practitioners must then perform algorithm selection and hyperparameter optimization to maximize the predictive performance of their model. If deep learning is used, the architecture of the neural network must also be chosen manually by the machine learning expert.
Each of these steps may be challenging, resulting in significant hurdles to using machine learning. AutoML aims to simplify these steps for non-experts, and to make it easier for them to use machine learning techniques correctly and effectively.
AutoML plays an important role within the broader approach of automating data science, which also includes challenging tasks such as data engineering, data exploration and model interpretation and prediction.[5]
Automated machine learning can target various stages of the machine learning process.[3] Steps to automate are:
There are a number of key challenges being tackled around automated machine learning. A big issue surrounding the field is referred to as "development as a cottage industry".[7] This phrase refers to the issue in machine learning where development relies on manual decisions and biases of experts. This is contrasted to the goal of machine learning which is to create systems that can learn and improve from their own usage and analysis of the data. Basically, it's the struggle between how much experts should get involved in the learning of the systems versus how much freedom they should be giving the machines. However, experts and developers must help create and guide these machines to prepare them for their own learning. To create this system, it requires labor intensive work with knowledge of machine learning algorithms and system design.[8]
Additionally, other challenges include meta-learning[9] and computational resource allocation.
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Source: Wikipedia. Article content is retrieved live through the MediaWiki API.
Automated machine learning (AutoML) is the process of automating the tasks of applying machine learning to real-world problems. It is the combination of automation and ML. AutoML potentially includes every stage from beginning with a raw dataset to building a machine learning model ready for deployment. AutoML was proposed as an artificial intelligence-based solution to the growing challenge of applying machine learning. The high degree of automation in AutoML aims to allow non-experts to make use of machine learning models and techniques without requiring them to become experts in machine learning. Automating the process of applying machine learning end-to-end additionally offers the advantages of producing simpler solutions, faster creation of those solutions, and models that often outperform hand-designed models. Common techniques used in AutoML include hyperparameter optimization, meta-learning and neural architecture search.
Machine Learning and Knowledge Extraction (MAKE) is a peer-reviewed open-access scientific journal covering research on machine learning, knowledge extraction and related areas of data-driven artificial intelligence. It is published by MDPI and was launched in 2019 with Andreas Holzinger as founding Editor-in-Chief. The journal publishes research articles, reviews, tutorials and short notes spanning topics such as data ecosystems, interactive and automated machine learning, explainable AI, privacy, graph learning and topological data analysis
Text Nailing (TN) is an information extraction method used in the fields of natural language processing (NLP), computational linguistics, and health informatics. It was developed to address challenges in extracting structured, analyzable data from large volumes of unstructured text such as clinical notes, research articles, and other narrative documents. Unlike fully automated machine learning systems, Text Nailing emphasizes a "human-in-the-loop" approach: a person interactively reviews small portions of text to identify expressions that are common, non-negated, and semantically informative. These "nailed expressions" are then converted into simplified alphabetical-only forms, creating consistent and homogeneous representations of the text. This hybrid strategy allows TN to combine the precision of expert-guided annotation with the scalability of computational techniques. The method has been applied to improve predictive modeling in medicine, enhance the performance of text classifiers, and reduce reliance on large manually labeled datasets.
Group method of data handling (GMDH) is a family of inductive, self-organizing algorithms for mathematical modelling that automatically determines the structure and parameters of models based on empirical data. GMDH iteratively generates and evaluates candidate models, often using polynomial functions, and selects the best-performing ones based on an external criterion. This process builds feedforward networks of optimal complexity, adapting to the noise level in the data and minimising overfitting, ensuring that the resulting model is accurate and generalizable. GMDH is used in such fields as machine learning, forecasting, optimization and pattern recognition, due to its ability to handle complex, nonlinear relationships in data. Its inductive nature allows it to discover patterns and interdependencies without requiring strong a priori assumptions, making it particularly effective for highly complex systems. By balancing model complexity and accuracy through self-organization, GMDH ensures that the model reflects the underlying relationships in data. This approach has influenced modern machine learning techniques and is recognised as one of the earliest approaches to automated machine learning and deep learning. A GMDH model with multiple inputs and one output is a subset of components of the base function (1): Y ( x 1 , … , x n ) = a 0 + ∑ i = 1 m a i f i {\displaystyle Y(x_{1},\dots ,x_{n})=a_{0}+\sum \limits _{i=1}^{m}a_{i}f_{i}} where fi are elementary functions dependent on different sets of inputs, ai are coefficients and m is the number of the base function components. In order to find the best solution, GMDH algorithms consider various component subsets of the base function (1) called partial models. Coefficients of these models are estimated by the least squares method. GMDH algorithms gradually increase the number of partial model components and find a model structure with optimal complexity indicated by the minimum value of an external criterion. This process is called self-organization of models. As the first base function used in GMDH, was the gradually complicated Kolmogorov–Gabor polynomial (2): Y ( x 1 , … , x n ) = a 0 + ∑ i = 1 n a i x i + ∑ i = 1 n ∑ j = i n a i j x i x j + ∑ i = 1 n ∑ j = i n ∑ k = j n a i j k x i x j x k + ⋯ {\displaystyle Y(x_{1},\dots ,x_{n})=a_{0}+\sum \limits _{i=1}^{n}{a_{i}}x_{i}+\sum \limits _{i=1}^{n}{\sum \limits _{j=i}^{n}{a_{ij}}}x_{i}x_{j}+\sum \limits _{i=1}^{n}{\sum \limits _{j=i}^{n}{\sum \limits _{k=j}^{n}{a_{ijk}}}}x_{i}x_{j}x_{k}+\cdots } Usually, more simple partial models with up to second degree functions are used. Other names include "heuristic self-organization of models" or "polynomial feedforward neural network". Jürgen Schmidhuber cites GMDH as one of the first deep learning methods, remarking that it was used to train eight-layer neural nets as early as 1971.
Before the 1921 destruction of Tulsa’s Greenwood District, Black residents had created a remarkable center of business and community life. The district included stores, professional offices, entertainment venues and homes owned by Black citizens. Understanding Greenwood means learning what was built—not only what was burned.
MORE →Shirley Chisholm, elected in 1968.
