Scott Schwartz

Bayesian Statistician experienced with: TensorFlow   - Keras & TensorFlow-Probability   - Epistemic and Aleatoric Uncertainty Modeling   - VAEs, Semi-Supervised Learning, Bayesian CNNs   - Bayes by Backprop (VI), Batch Normalization, Dropout   - Randomized prior functions & Gaussian Processes   - Generative Modeling, Normalizing Flows, Bijectors PyMC   - HMC and VI Python   - Pandas, Numpy, Scipy.Stats   - Scikit-Learn       - Pipelineing, Model Stacking, Regularization       - Bagging, Random Forests, SVMs, KNNs       - Quadratic/Linear Discriminant Analysis (Q/LDA)       - Latent Dirichlet Allocation (LDA)       - Clustering & Mixture Models       - PCA/SVD & NMF factorization   - XGBoost. SpaCy, Gensim, NLTK, textacy   - Statsmodels       - GLMs, VIFs Other   - R, Unix, LaTeX, Bash, Git, SQL, AWS, C++, Docker