there are many great resources to introduce the concepts and principles behind deep learning. some of the best online resources include: machine learning mastery, kaggle and youtube. there are also paid courses (eg data camp, fast.ai) and tutorials at sites such as udemy.
- deep learning with python
- deep learning with R
- francois chollet on twitter
- ian goodfellow on twitter
we use teams for communication, trello for project organisation and google docs for writing collaboratively. this ensures that there is only ever a single copy of any document. we use paperpile for reference management - greg can give you an access code.
my github repository, where most code should be available.
- 2017 Ruth Muir MSc Project
- Machine-Learning (neural network) driven algorithmic classification of Type 1 or Type 2 diabetes at the time of presentation significantly outperforms experienced clinician classification
- Parallel time-series analysis of HbA1c and Systolic BP using a recurrent neural network (RNN) stratifies for 1-year mortality in Type 2 Diabetes independent of age and parameter variability. DUK. London
- Parallel time-series analysis of HbA1c, Systolic Blood Pressure and BMI using Recurrent Neural Networks stratifies for 1-year mortality in T2DM, independent of age and parameter variability. ATTD. Vienna
- Time-series analysis of HbA1c using Recurrent Neural Networks stratifies for 1-year mortality in Type 1 and Type 2 Diabetes, independent of age and variability. ATTD. Vienna
- MyDiabetesIQ. machine learning for decision support & outcome prediction in diabetes. Automated Clinical Epidemiology Studies workshop, University of Birmingham. 141217