Web

Jammming

Portfolio-title

Jammming

The capstone project for the React course on Codecademy.

Jammming is a web app that uses the Spotify API to search for songs and create playlists that can then be saved to your account.

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AR

Rogers Continuous

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Rogers Continuous

An AR app that guides users through a step-by-step process of fixing their technical problems.

Rogers Continuous is built with Unity and used Vuforia Engine to power the Augmented Reality. The server used to run router diagnosis is hosted on a Raspberry Pi 3 and was written in Flask and C#.

This project was submitted to Elevate Tech Jam 2019.

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AR, Web

AR Periodic Table Trends Viewer

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AR Periodic Table Trends Viewer

This project was built with AR.js, a lightweight library for Augmented Reality. Using markers, 3D models can be shown in the camera feed.

For this project, 4 models are available: Atomic Radius (AR), Electron Affinity (EA), Electronegativity (EN), Ionization Energy (IE).

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Other

MacinTouch

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MacinTouch

An affordable hardware hack that brings amazing touchscreen capabilities to the MacBook and integrates with your existing workflows.

Using object detection algorithms and homography, it can detect when a user taps the screen. The hardware component was created with household items that amount to less than $5 in total.

This project was submitted to Hack The 6ix 2019.

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ML, Web

PoseNet Camera Feed Demo

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PoseNet Camera Feed Demo

With PoseNet running on TensorFlow.js anyone with a webcam-equipped desktop or phone can experience the technology right from within a web browser.

Since PoseNet on TensorFlow.js runs in the browser, no pose data ever leaves a user’s computer.

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ML

LSTM Nietzsche Text Generation

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LSTM Nietzsche Text Generation

Using RNNs and LSTMs that were trained on a corpus of writings by Nietzsche to sample and generate completely new sequences of text.

See the code
Other

TKS + Walmart Recommendation

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ML, Web

Skin Lesion Classifier

Portfolio-title

Skin Lesion Classifier

A classifier written in native Keras and converted to TensorFlow.js that can be used to classify seven different types of skin lesions.

This model uses the MobileNet architecture and was retrained on the HAM10000 dataset which contains approximately 10,000 documented images.

The model obtains an accuracy of approximately 80%, with a top-3 accuracy of approximately 94%.

Check it out Read the article
ML

MNIST Convolutional Neural Network

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MNIST Convolutional Neural Network

How CNNs work, real-world applications, and how to code CNNs in Python.

The MNIST dataset is a collection of 70,000 28x28 images of handwritten digits.

This model achieves an accuracy of approximately 98 to 99 percent.

Read the article See the code