TensorFlow: TensorFlow is an open-source software library for dataflow and differentiable programming across a range of tasks. It is used for machine learning and deep learning applications, such as neural networks and natural language processing.
PyTorch: PyTorch is an open-source machine learning library for Python, which provides tools for building and training deep neural networks. It has gained popularity due to its flexibility and ease of use.
Keras: Keras is a high-level neural networks API, written in Python and capable of running on top of TensorFlow, CNTK, or Theano. It allows for rapid prototyping of deep learning models, with a focus on user-friendliness, modularity, and extensibility.
scikit-learn: scikit-learn is a machine learning library for Python that provides simple and efficient tools for data mining and data analysis. It includes algorithms for classification, regression, clustering, and dimensionality reduction, among others.
Apache Spark: Apache Spark is an open-source big data processing framework that can handle both batch and real-time data processing. It includes libraries for machine learning and graph processing, making it a popular tool for large-scale data processing and analysis.
IBM Watson Studio: IBM Watson Studio is a cloud-based platform for building and deploying AI models. It includes tools for data preparation, model building, and model deployment, as well as a wide range of pre-built models and APIs.
Microsoft Azure AI: Microsoft Azure AI is a cloud-based suite of AI tools and services, including machine learning, cognitive services, and bot services. It provides a range of pre-built models and APIs, as well as tools for building and deploying custom models.
Google Cloud AI Platform: Google Cloud AI Platform is a cloud-based platform for building and deploying AI models. It includes tools for data preparation, model building, and model deployment, as well as a wide range of pre-built models and APIs.
H2O.ai: H2O.ai is an open-source machine learning platform that allows users to build and deploy AI models at scale. It includes tools for data preparation, model building, and model deployment, as well as a range of pre-built models.
TensorFlow.js: TensorFlow.js is a library for building and training machine learning models in the browser and on Node.js. It allows for the deployment of pre-trained models in web applications and can be used for a variety of applications, such as image classification and natural language processing.
PyTorch: PyTorch is an open-source machine learning library for Python, which provides tools for building and training deep neural networks. It has gained popularity due to its flexibility and ease of use.
Keras: Keras is a high-level neural networks API, written in Python and capable of running on top of TensorFlow, CNTK, or Theano. It allows for rapid prototyping of deep learning models, with a focus on user-friendliness, modularity, and extensibility.
scikit-learn: scikit-learn is a machine learning library for Python that provides simple and efficient tools for data mining and data analysis. It includes algorithms for classification, regression, clustering, and dimensionality reduction, among others.
Apache Spark: Apache Spark is an open-source big data processing framework that can handle both batch and real-time data processing. It includes libraries for machine learning and graph processing, making it a popular tool for large-scale data processing and analysis.
IBM Watson Studio: IBM Watson Studio is a cloud-based platform for building and deploying AI models. It includes tools for data preparation, model building, and model deployment, as well as a wide range of pre-built models and APIs.
Microsoft Azure AI: Microsoft Azure AI is a cloud-based suite of AI tools and services, including machine learning, cognitive services, and bot services. It provides a range of pre-built models and APIs, as well as tools for building and deploying custom models.
Google Cloud AI Platform: Google Cloud AI Platform is a cloud-based platform for building and deploying AI models. It includes tools for data preparation, model building, and model deployment, as well as a wide range of pre-built models and APIs.
H2O.ai: H2O.ai is an open-source machine learning platform that allows users to build and deploy AI models at scale. It includes tools for data preparation, model building, and model deployment, as well as a range of pre-built models.
TensorFlow.js: TensorFlow.js is a library for building and training machine learning models in the browser and on Node.js. It allows for the deployment of pre-trained models in web applications and can be used for a variety of applications, such as image classification and natural language processing.
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