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

ハイパラ探索自動化

This is a five-course specialization offered by deeplearning.ai.

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.

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.

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

Code for optimizing hyperparameters of Scikit-learn models

A library to build and run HyperOpt Hyper Parameter Optimization Schemes

Predict credit card approval status based on past customer details such as `Gender`, `Age`, `Debt`, `Married`, `BankCustomer`, `EducationLevel` etc.

CNN, RNN, LSTM, Attention , BLAH, BLAH from coursera [Anfrew NG]

It is a Problem Which I got During the ZS Data Science Challenge From Interview Bit Hiring Challenge Where I secured a 40th Rank out of 10,000 Students across India. It is a Dataset which requires Intensive Cleaning and Processing. Here I have Performed Classification Using Random Forest Classifier and Used Hyper Tuning of the Parameters to achi…

Deep Learning Specialization Course assignments and PDF Notes.

The goal is to predict whether or not a passenger survived based on attributes such as their age, sex, passenger class, where they embarked, etc.

What important conclusion a company and an employee can take out of Analysis and Predicting Salary