Devpost for Teams
Grow your developer ecosystem and promote your platform.
Drive innovation, collaboration, and retention within your organization.
This is a sample submission!
Creating An Interpretable CNN Model for Audio
A Neural Chatbot
Analysis on dehumanization in the language of New York Times articles
AlexNet performance, but on a microcontroller?!
Generating music using RNNs!
TempleFlow is a project that uses deep learning to play the classic game Temple Run 2
Automated package counting for shipping logistics
Text style transfer from Shakespearean English to Modern English
Generating classical piano music using Attention. Let's bring Bach to the Future!
We reimplemented the CVPR 2020 paper “U^2-Net: Going Deeper with Nested U-Structure for Salient Object Detection” using Tensorflow in Python.
We will design an agent that operates on a simple 2D top-down dungeon map, collecting coins while avoiding or attacking a simple automated opponent.
A MineSweeper Genius.
Reimplementation of a paper, using random walks and word2vec on graphs to predict transactions of cryptocurrency.
A full X-Ray classification pipeline which consists of removing bone shadows from the X-Rays followed by a classification problem on x-ray images of different diseases
Improving RNN seq2seq model with attention mechanism and scratchpad mechanism
We are reimplementing a neural network architecture that can detect the early onset of depression through users’ social media text.
Arabic sign language translation model
See how well we can predict the opening price of stocks using LSTM cells in a RNN.
We implement a paper that performs stock market prediction with three different neural networks using LSTM: single LSTM, stacked LSTM, and attention LSTM.
A Re-Implementation of Visual Question Answering
A language model based off of the GPT models (1,2, and 3)
To examine the effectiveness of SOTA meta-learning algorithms on top of pre-trained language model (i.e. BERT) on natural language understanding tasks and further on multimodal tasks
Reinforcement Learning with Value Distributions
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