How can you ensure the quality of your ANN project?

3 Train and tune your network

Once you have your architecture and framework, you can start training and tuning your network. Training involves feeding your data to your network and adjusting the weights and biases of the neurons to minimize the error between the output and the desired outcome. Tuning involves finding the optimal values for the hyperparameters of your network, such as the learning rate, batch size, regularization, and dropout. You should use a systematic and rigorous approach to train and tune your network, such as cross-validation, grid search, or Bayesian optimization, and monitor the performance and convergence of your network.

4 Evaluate and interpret your results

After you have trained and tuned your network, you need to evaluate and interpret your results. Evaluation involves comparing your network's performance with your predefined metrics and benchmarks, such as accuracy, precision, recall, or F1-score. Interpretation involves understanding how your network makes predictions and what features or patterns it learns from the data, such as using visualization, feature importance, or explainable AI techniques. You should evaluate and interpret your results objectively and critically, and identify the strengths and weaknesses of your network.

5 Document and communicate your findings

The final step is to document and communicate your findings. Documentation involves recording and organizing all the relevant information and details of your project, such as the goal, scope, data, architecture, framework, training, tuning, evaluation, interpretation, and limitations of your network. Communication involves presenting and sharing your findings with your stakeholders, such as your clients, colleagues, or peers, using clear and concise language, charts, graphs, or reports. You should document and communicate your findings clearly and effectively, and highlight the value and impact of your network.