How do you describe neural network results?

Neural Networks are networks of neurons and connection between them. In Data Science and Deep Learning we use Artificial Neural Network which consists various type of layers, neurons, activation functions and loss functions. Various kind of neural networks are ANN, RNN, CNN etc. These are basic neural nets, which combined can build a complex neural network architecture. it can be used to solve different problems in AI space.

I can speak from my experience as a data scientist when I say that it's important to realize that not all neural networks are the same. For particular tasks, different architectures have been designed. For instance, while Recurrent Neural Networks (RNNs) or Transformers are better suited for sequence-based tasks like language processing, Convolutional Neural Networks (CNNs) excel in tasks involving images. To achieve high performance, you must first identify the appropriate network type for your issue.

The first trainable artificial neural network was invented by Frank Rosenblatt in 1957, the model was called perceptron. The model had only one internal layer with adjustable weights and inputs and outputs layers. The model was developed to illustrate how the human brain process visual data and recognise objects.

As I see it, how your data is divided into training, validation, and test sets is just as important as the metric itself. If the data isn't representative or if the model has simply memorized the training data (overfitting), even the most accurate model according to your chosen metric may perform poorly in the real world. Use methods like k-fold cross-validation to take into account your model's robustness and generalizability.

Performance measures include: - Mean absolute error - Root nean squared error - Percent good classification In addition to accuracy and model performance, we also need to look at its architecture and size. There is no need to have hundreds of internal layers if only a fixed of layers can do the job.

Performance measure depends on the task in hand. there are two types of task mainly we work with- 1. Regression 2. Classification For regression, we have following metrics 1. RMSE 2. MSE 3. R2 Score For Classification task metrics are - 1. Accuracy 2. Precision 3. Recall 4. f1-score

Exploring the inner workings of your neural network can be greatly aided by visualization tools like TensorBoard, Matplotlib, or seaborn. For example, viewing the activation maps can provide insights into the features the network values. When explaining your model's operation to non-experts, these visualizations can be a potent communication tool.

Interpretation serves as a link between the mathematical complexity of a model and human understanding. LIME (Local Interpretable Model-agnostic Explanations) and SHAP (Shapley Additive Explanations) techniques can offer clearer insights into the way complex models make decisions. This is especially important in sensitive applications like healthcare or finance, where understanding the reasoning behind a model's choice can be just as crucial as the choice itself.

Include Explanations: Address the two primary types of model interpretability that end-users often seek: the significance of each input variable and how each variable influences specific predictions. Feature Importance: To elucidate the weightage of each input variable in the neural network's decision-making process, consider including a feature importance analysis. Specific Interpretation Methods: To break down how individual variables contribute to specific predictions or a group of them, employ advanced techniques like Shapley Value implementations for neural networks or LIME. These points should offer a deeper understanding and more explicit elaboration on presenting neural network outcomes.

From my perspective, iterative refinement is essential to developing high-performance models. Once you have a baseline model, hyperparameters can be systematically adjusted using techniques like grid search or random search. Don't undervalue the value of domain expertise either; knowing the data and the issue can help you make informed decisions about your network and data that frequently result in significant performance gains.

when we train any neural network we try to adjust weights of network defined, improve the metrics we have choose and reduce the loss of network. to improve the results, we can try with following strategies - 1. Weight initialisation function 2. Network number of layers and neurons 3. different activation functions 4. number of epochs of training