neural networks
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What is neural networks?
Neural networks are a set of algorithms, modeled loosely after the human brain, that are designed to recognize patterns. They interpret sensory data through a kind of machine perception, labeling or clustering raw input. They are commonly used for tasks such as image recognition, natural language processing, and predictive modeling.
What other technologies are related to neural networks?
neural networks Competitor Technologies
Random Forests
Random forests are an ensemble learning method for classification, regression and other tasks that operates by constructing a multitude of decision trees at training time and outputting the class that is the mode of the classes (classification) or mean prediction (regression) of the individual trees. They can be used as an alternative to neural networks.
decision trees
Decision trees are a non-parametric supervised learning method used for classification and regression. They can be used as an alternative to neural networks for certain tasks.
Random Forest
Random forests are an ensemble learning method that can be used as an alternative to neural networks.
Support Vector Machines
SVMs are supervised learning models with associated learning algorithms that analyze data used for classification and regression analysis. They can be used as an alternative to neural networks.
SVM
SVMs are supervised learning models used for classification and regression. They can be used as an alternative to neural networks.
Logistic Regression
Logistic regression is a statistical model that uses a logistic function to model the probability of a binary outcome. It can be used as an alternative to neural networks for binary classification problems.
Gradient Boosting
Gradient boosting is a machine learning technique for regression and classification problems, which produces a prediction model in the form of an ensemble of weak prediction models, typically decision trees. It can be used as an alternative to neural networks.
SVMs
SVMs are supervised learning models used for classification and regression. They can be used as an alternative to neural networks.
Number of organizations that mention technology
2,931
neural networks
673
Random Forests
1,136
decision trees
1,677
Random Forest
384
Support Vector Machines
1,368
SVM
1,081
Logistic Regression
466
Gradient Boosting
188
SVMs
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neural networks Complementary Technologies
Natural Language Processing
Neural networks, especially recurrent neural networks and transformers, are commonly used in NLP tasks. They can be used together to create advanced NLP systems.
Principal Component Analysis
PCA is a dimensionality reduction technique that can be used to preprocess data before feeding it into a neural network, improving performance and reducing computational cost.
reinforcement learning
Neural networks are often used as function approximators in reinforcement learning to handle complex state spaces and action spaces.
Number of organizations that mention technology
2,931
neural networks
7,013
Natural Language Processing
160
Principal Component Analysis
2,379
reinforcement learning
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Which job functions commonly mention neural networks?
Data Scientist
1.2% of all Data Scientist jobs mention neural networks
View 2.8k
jobs on Sumble
AI Engineer
1.1% of all AI Engineer jobs mention neural networks
View 924
jobs on Sumble
Machine Learning
0.8% of all Machine Learning jobs mention neural networks
View 812
jobs on Sumble
Applied Scientist
0.6% of all Applied Scientist jobs mention neural networks
View 71
jobs on Sumble
Data, Analytics & Machine Learning
0.3% of all Data, Analytics & Machine Learning jobs mention neural networks
View 5.1k
jobs on Sumble
Which organizations are mentioning neural networks?
Johnson & Johnson
Health Care and Social Assistance
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