Neural Network Classification and Ethical Implications

Implement a deep neural network on the MNIST dataset using TensorFlow or PyTorch

Explore and justify the choice of network architecture, layers, and hyperparameters

Explore and discuss the ethical implications of model predictions, particularly focusing on issues like faimess, accountability, and transparency in Al$.$

Comprehensive Model Analysis

Compare all classifiers implemented in previous tasks.

Perform a statistical analysis of their performance metrics $($accuracy$,$ precision, recall, F$1$ score$)$ to determine the best model.

Discuss each model's suitabilify for various real$-$world applications and suggest practical improvements or altemative approaches.

Advanced Regression Analysis

Import the california$\_$housing dataset from the sklearn datasets

Apply multiple regression models $($Linear$,$ Polynomial, and Ridge$/$Lasso Regression$)$ to the Calfornia housing dataset. Discuss the benefits and limitations of each under various data conditions.

Evaluate model performance with both numerical and graphical analysis, looking at error metrics and residual plots.

Investigate the impact of different polynomial degrees on performance and overfitting.