The problem lies in the fact that understanding the functions and concepts of gradient descent in neural networks poses a challenge. It is difficult to grasp the complex multi-layer neural networks and how their parameters function. Particularly, the role that weight changes and functions have on the operation of the neural network is unclear. Additionally, there is uncertainty about overfitting and interpreting distributions. In the face of these difficulties, playing with various available datasets or your own data could be helpful.

Playground AI takes on the challenge of understanding neural networks and gradient descent by providing user-friendly and interactive visual representations. With this tool, users can change hyperparameters to see direct effects on network functions and thus better understand the impact of weight changes and function adjustments. Playground AI also provides a prediction feature that visualizes how changes within the network affect its operation. Through the opportunity to experiment with different data sets, or introduce their own data, users can learn and gain practical experience. Visualization of distributions also helps to understand their interpretation. In addition, the tool provides explanations and warnings about overfitting to better understand and avoid this phenomenon. This interactive and visual learning effectively promotes and improves the understanding of neural networks and gradient descent.