A data miner currently focusing on Cybersecurity domain and interested in detecting malwares via novel Deep Learning approaches, and designing end-to-end systems with near-real time predictive performance.
Build and train deep neural networks, identify key architecture parameters, implement vectorized neural networks and deep learning to applications. Train test sets, analyze variance for DL applications, use standard techniques and optimization algorithms, and build neural networks in TensorFlow. Build a CNN and apply it to detection and recognition tasks, use neural style transfer to generate art, and apply algorithms to image and video data. Build and train RNNs, work with NLP and Word Embeddings, and use HuggingFace tokenizers and transformer models to perform NER and Question Answering.
Frame a business use case as a machine learning problem. Gain a broad perspective of machine learning and where it can be used. Convert a candidate use case to be driven by machine learning. Recognize biases that machine learning can amplify.