Model performance and tuning analysis conducted on the CIFAR10 and CIFAR100 datasets. Convolutional Neural Network (CNN), Gated Multilayer Perceptron (gMLP), and Vision Transformer (ViT) model architectures are utilized. The study is built using PyTorch, PyTorch Lightning for clean and concise code and Optuna for hyperparameter tuning.

🔥 A tool for visualizing and tracking your machine learning experiments. This repo contains the CLI and Python API.

Determined is an open-source machine learning platform that simplifies distributed training, hyperparameter tuning, experiment tracking, and resource management. Works with PyTorch and TensorFlow.

Automated Machine Learning on Kubernetes

Large scale and asynchronous Hyperparameter and Architecture Optimization at your fingertips.

This project is to classify emails as spam or not spam using various machine learning models. Hyperparameter tuning is performed to optimize model performance.

SkyPilot: Run LLMs, AI, and Batch jobs on any cloud. Get maximum savings, highest GPU availability, and managed execution—all with a simple interface.

This pdf shows some methods to fine tune the hyperparameters of a Neural Network to increase it's performance. It also puts light on some of the common problems related to Neural Networks along with their solutions.

This repository contains the code, documentation, and datasets for a comprehensive exploration of machine learning techniques to address class imbalance. The project investigates the impact of various methods, like ADASYN, KMeansSMOTE, and Deep Learning Generator, on classification performance while effectively demonstrating benefits of pipelining.

Purely functional genetic algorithms for multi-objective optimisation

We harness the power of machine learning and data analysis to real challenges in the copper industry. Our documentation covers data preprocessing, feature engineering, classification, regression, and model selection. Discover how we've optimized predictive capabilities for manufacturing solutions.

Trained a regularized Logistic Regression model for classifying machine failures using nothing but NumPy and Linear Algebra!

Describe and optimize data

Here the prediction and analysis of student scores using selected features is done entirely by linear regression machine learning algorithm. This project covers all methods of linear regression theory.

Hyper parameter tuning framework.

Python-based research interface for blackbox and hyperparameter optimization, based on the internal Google Vizier Service.

SMAC3: A Versatile Bayesian Optimization Package for Hyperparameter Optimization

The project focuses on predicting the presence of heart disease based on various medical variables such as age, sex, and cholesterol level. Decision tree models are employed for this classification task, exploring both basic and hyperparameter-tuned versions.

The objective of this project is to predict the number of bicycles needed to be made available each hour in order to make the service as efficient as possible

Streamlining reinforcement learning with RLOps. State-of-the-art RL algorithms and tools.