1. TensorFlow: https://www.tensorflow.org/ Use: TensorFlow is an open-source machine learning platform that is used for a variety of applications, including image and speech recognition, natural language processing, and predictive analytics.
2. PyTorch: https://pytorch.org/ Use: PyTorch is another popular open-source machine learning platform that is particularly well-suited for deep learning and neural network applications.
3. Microsoft Azure Machine Learning: https://azure.microsoft.com/en-us/services/machine-learning/ Use: Microsoft Azure Machine Learning is a cloud-based platform that allows users to build, deploy, and manage machine learning models.
4. IBM Watson Studio: https://www.ibm.com/cloud/watson-studio Use: IBM Watson Studio is a cloud-based platform that allows users to build and deploy machine learning models using a variety of tools and frameworks.
5. ai: https://www.h2o.ai/ Use: H2O.ai is a machine learning platform that is particularly well-suited for data scientists and analysts who need to build and deploy predictive models quickly and easily.
6. OpenAI: https://openai.com/ Use: OpenAI is an artificial intelligence research organization that develops advanced AI models and tools for a variety of applications, including natural language processing, computer vision, and robotics.
7. Amazon SageMaker: https://aws.amazon.com/sagemaker/ Use: Amazon SageMaker is a cloud-based platform that allows users to build, train, and deploy machine learning models using a variety of tools and frameworks.
8. Keras: https://keras.io/ Use: Keras is an open-source deep learning framework that allows users to build and train neural networks quickly and easily.
9. Dialogflow: https://cloud.google.com/dialogflow Use: Dialogflow is a natural language processing platform that allows users to build conversational interfaces for a variety of applications, including chatbots and voice assistants.
10. Google Cloud AI Platform: https://cloud.google.com/ai-platform Use: Google Cloud AI Platform is a cloud-based platform that allows users to build, train, and deploy machine learning models using a variety of tools and frameworks. It also provides tools for data preprocessing, model evaluation, and model monitoring.

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