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Name: Abhyuday Desai
Type: User
Company: Ready Tensor, Inc.
Bio: AI/ML entrepreneur with a passion for making AI accessible and practical for all.
Location: San Diego, CA
Name: Abhyuday Desai
Type: User
Company: Ready Tensor, Inc.
Bio: AI/ML entrepreneur with a passion for making AI accessible and practical for all.
Location: San Diego, CA
LightGBM Classifier with LIME explanations for Multi-class Classification
Logistic Regression Classifier in SciKitLearn for Multi-class Classification - Base problem category as per Ready Tensor specifications.
Logistic Regression Classifier in TensorFlow/Keras for Multi-class Classification - Base problem category as per Ready Tensor specifications.
Multi-layer Perceptron Classifier in SciKitLearn for Multi-class Classification
Naive Bayes Gaussian Classifier in SciKitLearn for Multi-class Classification
Multi-class Classifier in PyCaret for Multi-Class Classification
Random Forest Classifier in SciKitLearn for Multi-class Classification - Base problem category as per Ready Tensor specifications.
Simple ANN-based Classifier in PyTorch with Shapley explanations for Multi-class Classification
Artificial Neural Network Classifier in TensorFlow/Keras for Multi-class Classification - Base problem category as per Ready Tensor specifications.
Support Vector Classifier in SciKitLearn with LIME explanations for Multi-class Classification - Base problem category as per Ready Tensor specifications.
XGB Classifier in SciKitLearn with LIME explanations for Multi-class Classification
AutoRecommender algorithm built in TensorFlow for Recommender - Base problem category as per Ready Tensor specifications.
Clustered Matrix Factorization implemented with Neural Network build in TensorFlow for Recommender - Base problem category as per Ready Tensor specifications.
Matrix Factorizer trained using gradient descent built in TensorFlow/Keras for Recommender - Base problem category as per Ready Tensor specifications.
Matrix Factorizer trained using gradient descent built in PyTorch for Recommender - Base problem category as per Ready Tensor specifications.
Matrix Factorizer trained using gradient descent built in TensorFlow/Keras for Recommender - Base problem category as per Ready Tensor specifications. Model is implemented as a neural network. In addition to matrix factorization, model also has a residual layer which is non-linear.
Co-Clustering algorithm built in Scikit-Surprise for Recommender - Base problem category as per Ready Tensor specifications.
Non-Negative Matrix Factorization built in Scikit-Surprise for Recommender - Base problem category as per Ready Tensor specifications.
Slope One algorithm built in Scikit-Surprise for Recommender - Base problem category as per Ready Tensor specifications.
Singular Value Decomposition built in Scikit-Surprise for Recommender - Base problem category as per Ready Tensor specifications.
Singular Value Decomposition++ built in Scikit-Surprise for Recommender - Base problem category as per Ready Tensor specifications.
Regression using Adaboost regressor in Scikit-Learn
ANN regression with Trainable Activation in TensorFlow/Keras for Regression.
Linear Regression using Interpret ML and Scikit-Learn Elastic Net as the linear class in model interpretability for Regression.
elasticnet for linear regression, implemented using sklearn
ElasticNet Regressor for Regression using TensorFlow/Keras
LightGBM Regressor using Scikit-Learn and LIME for Regression
General Linear Regression Algorithm in R
Regressor in PyCaret for Regression - Base problem category
Random Forest Regressor in SciKitLearn for Regression - Base problem category as per Ready Tensor specifications.
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