i need a machine learning python developer to do this project for me

Objective: Implement simulations to explore computational models of visual concept learning. The easiest approach would be to base your simulations on the Python code presented during the Lab practices; however, you might also explore you own ideas and implementations (get in touch with the instructors by email for feedback). As training / testing datasets you could use one or more of the following: • EMNIST [[login to view URL]] • Omniglot [[login to view URL]] • Caltech 256 [[login to view URL]] • Feel free to explore other datasets, keeping in mind that large datasets might require more computational resources. The maximum score for the individual project will be 10 points, divided according to the following tasks: • Perform linear read-outs at different levels of the model hierarchy to investigate whether sensory representations become increasingly more disentangled at the deepest layers of the network [2 points]. • Analyze the internal representations developed by the model, for example by exploiting hierarchical clustering and/or feature visualization methods [2 points]. • Visualize confusion matrices and psychometric curves to investigate the kind of errors made by the model; psychometric curves can be created by adding an increasing amount of noise to the test data [2 points]. • Explore the response to adversarial attacks and/or the capability of few-shot learning [2 points]. • Clearly explain the details of your implementation (e.g., model architecture, training and testing methodology), motivating your choices and critically discussing the results with respect to the topics covered during the course [2 points].