All About You:
:• Demonstrated passion for AI competing in sponsored challenges such as Kaggle
• Previous experience with or exposure to:
•Deep Learning algorithm techniques, open source tools and technologies, statistical tools, and programming environments such as Python, R, and SQL
• •Big Data platforms such as Hadoop, Hive, Spark, GPU Clusters for deep learning
•Classical Machine Learning Algorithms like Logistic Regression, Decision trees, Clustering (K-means,
•Hierarchical and Self-organizing Maps), TSNE, PCA, Bayesian models, Time Series ARIMA/ARMA, •Recommender Systems - Collaborative Filtering, FPMC, FISM, Fossil
•Deep Learning algorithm techniques like Random Forest, GBM, KNN, SVM, Bayesian, Text Mining techniques, Multilayer Perceptron, Neural Networks – Feedforward, CNN, LSTM’s GRU’s is a plus. •Optimization techniques – Activity regularization (L1 and L2), Adam, Adagrad, Adadelta concepts; Cost •Functions in Neural Nets – Contrastive Loss, Hinge Loss, Binary Cross entropy, Categorical Cross entropy; developed applications in KRR, NLP, Speech and Image processing
•Deep Learning frameworks for Production Systems like Tensorflow, Keras (for RPD and neural net architecture evaluation), PyTorch and Xgboost, Caffe, and Theono is a plus
• Concentration in Computer Science