R

RNN

Real-time Gender and Age Recognition from Audio

MLP and CNN to classify TinyImage30 dataset. Fine Tuned models. Applied Grad-Cam to identify parts of the image that highly impact the classification based on model convolution gradients. Feature-2-Seq RNN encoder/decoder network trained on the COCO dataset. The produced model is able to predict reasonable captions for provided test images.

Develop a cool feature in the smart-TV that can recognize five different gestures performed by the user which will help users control the TV without using a remote.

Build a Bitcoin predictive model. (Inputs : Klines, Google Trend data. Future inputs implementation : Liquidations data)