Hi, I'm Simone. I do full-stack web engineering with machine learning.

If you are interested in working with me please get in touch!

  • Mentoring for Sequence Models with deeplearning.ai

    I was invited to be a volunteer mentor on the Sequence Models course which is a part of the deeplearning.ai Deep Learning Specialization on Coursera. This is a course associated with Stanford University. The course covers Recurrent Neural Networks for Natural Language Processing. I got the invitation by email a few weeks after I completed the 5 courses in the Specialization in May 2018. I did this as a follow-up to the ever-popular Machine Learning course by the same instructor.

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  • A Django REST API

    Django is a scalable and open-source Python web framework for building custom web applications. It was built to be fast and flexible on a variant of the MVC pattern called Model-View-Template. It has many features built in like an admin dashboard, user authentication, and form validation.

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  • Up and Running with React

    React is a popular and in-demand front-end JavaScript web framework created by Facebook. It creates very responsive (meaning quick) web applications which do not require a full page reload to update the data. Thus, it can give the impression of using a desktop application rather than one based in a browser. These web apps can be either single or multi-page. React’s nearest competitor is Angular which is backed by Google.

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  • Apache Spark with a Recommender System

    Apache Spark is a popular framework for distributed computing and big data. It can be used with Java, Scala, R and Python via its high-level APIs. The techniques and patterns of the Python API (PySpark) are quite similar to those of Pandas and Scikit-Learn as previously explored. In this post, we use PySpark to build a recommender system.

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  • Linear Classification
    Support Vector Machines

    Support Vector Machines are a popular type of Supervised Learning algorithm. They perform well on smaller datasets and are thus a viable alternative when there is not enough data to train an algorithm which is data hungry like a deep net. They can be used for both Classification and Regression tasks in Scikit-Learn.

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  • Random Forest Regression Pt. 4
    Training using One Feature with Grid Search and Randomised Grid Search

    This is Pt. 4 in the series covering Random Forest Regression to predict the price of RY stock. It follows from Pt. 3 on Feature Engineering. In this post, training is done using the estimators and tools provided by Scikit-Learn.

    We begin with a discussion of the theoretical concepts needed to undergo this process. These include hyperparameters, Grid Search, and pickling. Then, the models are trained using one feature with both Grid Search and Randomised Grid Search. The hypothesis that Randomised Grid Search is generally the...

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