Yashraj Pathak
Resume | LinkedIn | GitHub
ML Practitioner | Data Analyst | IIIT-B | Liverpool John Moores University | Data Scientist | Python | Actively Looking for Deep Learning/ ML Engineer role
Data Science Portfolio
Machine learning
Gesture Recognizer
Gesture recognition via CNN neural network implemented in Keras + Theano + OpenCV
Key Requirements: Python 3.6.1 OpenCV 3.4.1 Keras 2.0.2 Tensorflow 1.2.1 Theano 0.9.0 (obsolete and not supported any further)
Suggestion: Better to download Anaconda as it will take care of most of the other packages and easier to setup a virtual workspace to work with multiple versions of key packages like python, opencv etc.
Compared to a classical approach, using a Recurrent Neural Networks (RNN) with Long Short-Term Memory cells (LSTMs) require no or almost no feature engineering. Data can be fed directly into the neural network who acts like a black box, modeling the problem correctly. Other research on the activity recognition dataset can use a big amount of feature engineering, which is rather a signal processing approach combined with classical data science techniques. The approach here is rather very simple in terms of how much was the data preprocessed.
Let’s use Google’s neat Deep Learning library, TensorFlow, demonstrating the usage of an LSTM, a type of Artificial Neural Network that can process sequential data / time series.
