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.

Taidot: Keinoäly, Machine Learning (ML), Koodaus, Raportin kirjoittaminen

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Tietoa työnantajasta:
( 0 arvostelua ) Poplar, United Kingdom

Projektin tunnus: #20116889

Myönnetty käyttäjälle:

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£50 GBP 3 päivässä
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£250 GBP 7 päivässä
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£45 GBP 3 päivässä
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