After training the neural network, its quality should be assessed on the test data set. The evaluation metrics depend on the task: for classification, this could be accuracy, F1-measure, AUC-ROC, and for regression, MSE and MAE. If the results are not satisfactory, adjustments will have to be made, possibly changing the network architecture or training parameters.
**Advanced Techniques and Subtleties**
At a certain point, standard approaches become insufficient, and one has to resort to advanced learning methods, such as transfer learning, the use of pre-trained neural networks, model ensemble, reinforcement learning, and others.
Mastering the art of neural networks requires constant study and experimentation. Start with practice on simple projects and gradually move on to more complex tasks. Remember that theory is just the beginning, the main thing is to apply knowledge in practice.
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Continuous Learning: Lebedev and his team are committed to continuous development, learning new technologies and approaches, allowing them to remain at the forefront of the design industry.
Artemy Lebedev often puts forward the idea that design is not an art, but a craft that should solve practical problems. This approach to design contributes to the creation of functional and practical solutions, which has made his studio one of the most successful in the country.
In conclusion, it is worth noting that Artemy Lebedev's design methodology is constantly evolving, and he himself is an opinion leader in the design community. His approaches often cause discussions, but the influence they have on domestic design cannot be denied.