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TensorFlow Playground, at playground.tensorflow.org, is an interactive web-based platform provided by TensorFlow, an open-source machine learning framework developed by the TensorFlow team at Google. The TensorFlow Playground is designed to help users understand and experiment with neural networks in a visual and interactive way. It is used by college students and other ML learners to understand the basics of neural networks and how different parameters and architectures can affect their performance.  

However, it doesn't use TensorFlow specifically. While TensorFlow is a popular open-source machine learning framework developed by the Google Brain team, the TensorFlow Playground itself is more focused on providing a user-friendly environment for educational purposes rather than serving as a practical implementation using TensorFlow. To practice neural networks with TensorFlow, we need to explore the official TensorFlow tutorials and documentation, which provide hands-on examples and guidance for building and training neural networks using TensorFlow.

The key features of the TensorFlow Playground include: 

1. Interactive Neural Network Playground: 

   Users can build, modify, and visualize neural networks in real-time using an intuitive interface. This includes adjusting parameters such as the number of hidden layers, neurons, activation functions, and learning rates. 

2. Real-time Visualization: 

   The platform provides immediate visual feedback on how changes to the neural network architecture and parameters impact the learning process. Users can observe the training process and how the decision boundaries evolve over time. 

3. Built-in Datasets: 

   The Playground comes with pre-loaded datasets that users can use for training and testing their neural networks. These datasets often include simple classification problems to demonstrate the capabilities of neural networks in pattern recognition tasks. 

4. Learning and Exploration: 

   The Playground is educational and is often used as a tool for learning about the basics of neural networks, deep learning, and machine learning concepts. Users can experiment with different configurations to gain insights into how neural networks function. 

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