hyper-parameter optimization directly from code

Tuner Implementation of Freeze-Thaw Bayesian Optimization

Use NLP techniques to improve baseline model performance of a Question Answering problem

Tuner Implementation of Hyperband: A Novel Bandit-Based Approach to Hyperparameter Optimization

Predict MPG value for a vehicle

ハイパラ探索自動化

A deployed machine learning model that has the capability to automatically classify the incoming disaster messages into related 36 categories. Project developed as a part of Udacity's Data Science Nanodegree program.

In this Deep Learning Model, we optimized the hyperparameters of a CNN model by using the Keras tuner library.

Teamwork study project

ML Supervised- Developed a regression model using algorithms such as Random Forest and XGBoost to predict the hourly demand of bikes.Performed hyperparameter tuning techniques and achieved R2 score of 92% using XGBoost model and reduced the public waiting time significantly.

Python scripts of Random Forest

Check for best hyperparmeters inthe following models: • K-nn (based on Euclidian distance) • Logistic regression • Support Vector Classifier o Linear SVC o Polynomial SVC of degree m (m will be set in the code) o Gaussian SVC

Regression with Linear Regression, Decision Trees, KNN and Random Forest.

NLP analysis of dialogue rules on Reddit conducted during BSc. Code sample includes latent Dirichlet allocation and hyperparameter optimisation conducted in R.

Bilevel Hyperparameter Optimization with Implicit Differentiation

Bayesian Hyperparameter Optimisation for Resnet Architecture on CIFAR-10 Dataset.

Image Recognition including 3 phases; feature extraction, data exploration and model selection, and performance estimation

All your Data Science Experiments in one place

Metalearning for Hyperparameter Optimization

Distributed hyperparameter optimization made easy