A deep learning optimization enthusiast and Sustainable AI advocate.An impactful applied researcher enabling AI innovations for real-world challenges.
Learned both the classical machine learning skills and the state-of-the-art deep learning techniques needed to build NLP systems. Equipped to design applications that perform question-answering and sentiment analysis, create tools to translate languages, and summarize text!
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.
The focus of the problem is to perform multiple choice question answering using BERT (a state-of-the-art transformer network). This is achieved by alleviating the ability of BERT to support large text corpus by extracting the highest influence sentences through a semantic similarity model. Our approach outperformed the leading models and ranked first in the MovieQA challenge leaderboard with test accuracy of 87.79%.
Implement models from Data-Mining’s Learning to Rank field to predict the most shared articles in a week using a provided data set.
A peek inside boosting classification performance by locking false alarms.