Hi Anne,
You asked some good questions.
Based on your first question, it seems like you are analyzing the performance of a neural network model by comparing the root mean square error between training and validation data at different neurons, as well as examining the difference between average predicted and actual data. In the first figure, you correctly identified that the root mean square error is slightly higher at the 30th neuron compared to the first neuron (0.08038 vs. 0.07978). This difference may indicate a slight increase in prediction error as the neural network processes more complex patterns or features. Moving on to the second figure, you mentioned that the difference between the average predicted and actual data is 0.000649. This value represents the discrepancy between what your model predicts and the actual ground truth, suggesting a small margin of error in your predictions. To optimize your neural network model based on these findings using MATLAB functions, you can consider several strategies such as adjusting hyperparameters, increasing training data size, implementing regularization techniques, or fine-tuning the network architecture. For example, you could experiment with different activation functions, learning rates, batch sizes, or regularization methods like L1 or L2 regularization to improve model performance and reduce prediction errors. Additionally, you may want to explore techniques like early stopping or dropout to prevent overfitting and enhance generalization. By iteratively testing and tweaking these parameters using MATLAB's optimization functions and monitoring the model's performance metrics, you can fine-tune your neural network to achieve better accuracy and minimize errors in predictions. Remember to validate your optimized model on unseen data to ensure its robustness and generalizability.
Regarding your second question, additionally , how will you know if the number of neurons become overfitting? And if I run a statistical test between the actual data and ANN data outcome, which part of the data should I use to compare?
In MATLAB, several techniques can help detect overfitting in neural networks. One common approach is to monitor the network's performance on a separate validation dataset during training. If the network's performance on the validation set starts to degrade while the training performance continues to improve, it may indicate overfitting.
Another method involves using regularization techniques like weight decay or dropout during training. These techniques help prevent the network from memorizing the training data too closely, thus reducing the risk of overfitting.
To conduct a statistical test between actual data and ANN outcomes in MATLAB, you need to carefully select the data subsets for comparison. Typically, you would divide your dataset into three parts: training data, validation data, and test data. When comparing the actual data with ANN predictions, it is advisable to use the test data subset.
The test data subset serves as an independent dataset that the model has not seen during training or validation. By evaluating the model's performance on the test data, you can assess how well the neural network generalizes to unseen data and make meaningful comparisons between the actual data and ANN predictions.You can use the predict function to generate predictions from your trained neural network model and then compare these predictions with the actual data using statistical tests like hypothesis testing, mean squared error, or correlation analysis.
For more information regarding predict function, please refer to https://www.mathworks.com/help/stats/linearmodel.predict.html
Regarding your last question you asked, Lastly, what would be a good reference book/website/video to understand ANN better?
One highly recommended reference book for understanding ANNs is "Neural Networks and Deep Learning" by Michael Nielsen. This book offers a detailed yet accessible explanation of the fundamentals of neural networks and their applications in deep learning. It covers both theoretical concepts and practical implementations, making it an excellent resource for beginners and advanced learners alike. For websites, "Deep Learning Book" by Ian Goodfellow, Yoshua Bengio, and Aaron Courville is a widely recognized online resource that provides a comprehensive overview of deep learning techniques, including ANNs. The website offers free access to the entire book, allowing readers to delve into topics such as neural network architectures, training algorithms, and applications in various domains. In terms of videos, the YouTube channel "3Blue1Brown" has an exceptional series titled "Neural Networks" that visually explains the concepts behind ANNs in an engaging and intuitive manner. The videos cover topics like feedforward neural networks, backpropagation, and convolutional neural networks, offering a deeper understanding of how ANNs work. These recommended resources offer a combination of theoretical knowledge and practical insights into ANNs, catering to different learning preferences. By exploring these sources, you can gain a solid foundation in understanding artificial neural networks and their applications across diverse fields.
However, it is important to supplement your learning with hands-on practice through coding exercises and projects to reinforce your understanding of ANNs. Platforms like TensorFlow Playground and Kaggle provide interactive environments for experimenting with neural networks and applying them to real-world datasets. Combining theoretical knowledge with practical experience will help you master the intricacies of ANNs effectively.
Please let me know if you need further assistance or help.
