The problem is that it is a challenging task to understand in detail the operation of neural networks, the functioning of gradient descent, various distributions and overfitting. There is a need for a tool that simplifies complexity and promotes solid understanding through visual learning and experimentation. Additionally, there is a need to play with different hyperparameters and observe their effects. There is also a need for a tool that offers the ability to manipulate data in order to see how changes affect the model's behavior. Finally, the tool should also offer prediction capabilities to facilitate a deeper understanding of how changing weights and functions impact the operation of the neural network.

Playground AI provides an interactive solution by allowing users to visually explore various elements of neural networks. It illustrates functions such as gradient descent and overfitting to reduce complexity and promote intuitive understanding. Users can experiment with various hyperparameters and directly observe their effects visually to get a better feel for their role. Moreover, the tool offers features for manipulating data for practical application. It also provides immediate predictions as feedback, making the learning process more potent. This approach enables users to recognize the operational behavior of the neural network and makes the effects of manipulating weights and functions transparent.