[NeurIPS'21] "AugMax: Adversarial Composition of Random Augmentations for Robust Training" by Haotao Wang, Chaowei Xiao, Jean Kossaifi, Zhiding Yu, Animashree Anandkumar, and Zhangyang Wang.
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normalization-techniques
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Official implementation for "Pure Noise to the Rescue of Insufficient Data: Improving Imbalanced Classification by Training on Random Noise Images" https://arxiv.org/abs/2112.08810
machine-learning deep-learning batch-normalization imbalanced-data gaussian-noise normalization-techniques augmentations low-data-regime oversampling-technique
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Implementation of some famous data mining algorithms in C++11 boost
data-mining-algorithms algorithms-implemented regression-analysis correlation-analysis mining-algorithm dataminning five-number-summary normalization-techniques regression-equation bining-data-mining data-mining-lab data-mining-sauravgore
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- C++
Learning about the algorithms used in machine learning, along with techniques for training and testing models.
python nlp html learning data-science gradient-descent backpropagation-learning-algorithm ia feature-engineering regression-models loss-functions metrics-visualization normalization-techniques optimizer-algorithms neuronal-networks preprocessing-data
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- Jupyter Notebook
Evaluating the Robustness of Biomedical Concept Normalization
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- Python
This project analyzes e-commerce order fulfillment using Advanced SQL Techniques and Python-based visualization to uncover insights on sales trends, customer segmentation, shipping cost optimization, and payment preferences.
data-visualization indexing partitioning database-design business-analytics customer-segmentation normalization-techniques common-table-expressions window-functions-in-sql
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- HTML
Continuous Norming with R
biometrics percentile taylor-series psychometrics growth-curve normalization-techniques norm-scores continuous-norming regression-based-norming norm-tables beta-binomial
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- R
Analyze the customer data, build a neural network to help the operations team identify the customers that are more likely to churn, and provide recommendations on how to retain such customers
tensorflow nn eda batch-normalization keras-classification-models adam-optimizer model-training numpy-library pandas-library model-tuning sgd-optimizer regularization-methods normalization-techniques model-optimization matplotlib-library regularization-dropout seaborn-library
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The projects are part of the graduate-level course CSE-560 : Data Models and Query Language [Fall 2019 @ UB_SUNY] ... Course Instructor : Jan Chomicki (https://cse.buffalo.edu/~chomicki/)
mysql sql-query xquery xml-schema database-schema er-model query-language relational-algebra relational-model er-diagram database-design ddl-commands advanced-sql normalization-techniques range-encode dtd-transformation dml-commands sql2 sql3 sql-recursion
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The goal of this project is to develop an automatic classification system for exotic fruits based on numerical features using the K-nearest neighbors (K-NN) algorithm
python machine-learning numpy scikit-learn pandas seaborn matplotlib confusion-matrix knn anomalies-detection multiclass-classification knn-classification gridsearchcv auc-roc-curve matplotlib-pyplot normalization-techniques stratified-cross-validation hold-out-cross-validation
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- Jupyter Notebook
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