Artificial intelligence using Jupiter notebook (ML)

Choose a deep learning problem (binary classification, multiclass classification or regression) You can use any dataset (including MNIST, Reuters, IMDB and Boston Housing Price). Build a deep learning model. Investigate different hyper-parameter settings. Log your findings in a Jupyter notebook. Convert your Jupyter notebook into a report. The report should be structured as follows Introduction. State the purposes of your experiments Methods. This where the workflow has happened. Results. Graphs and tables, with commentary. Evaluation. Discuss your findings and form a conclusion Use markdown headings, subheadings, subsubheadings etc. to structure your report. Credit will be awarded for: report structure and quality as a document the extent of the research a clear objective a systematic investigation good methodology understanding of deep learning. Extra credit will be given for: clean, modular code a dataset other than one mentioned above extremely thorough examination of a model on a single dataset consideration of statistical uncertainty when comparing hyper-parameter settings comparison of a hypothesis space on different datasets of a similar type any work or DL understanding that reaches beyond the topics and concepts discussed so far. Zip your Jupyter notebook and any additional documents, for example, images You must submit a zip file.