Dataconomy

JUNE 29, 2023

Some machine learning packages focus specifically on deep learning, which is a subset of machine learning that deals with neural networks and complex, hierarchical representations of data. Let’s explore some of the best Python machine learning packages and understand their features and applications.

Machine Learning 113

Machine Learning Machine Learning Deep Learning Deep Learning 113

DataRobot

DECEMBER 20, 2021

Image recognition is one of the most relevant areas of machine learning. Deep learning makes the process efficient. However, not everyone has deep learning skills or budget resources to spend on GPUs before demonstrating any value to the business. Submit Data. DataRobot Visual AI. Run Autopilot.

Clustering 97

Clustering Deep Learning Deep Learning Exploratory Data Analysis 97

Pickl AI

FEBRUARY 12, 2024

What is cross-validation, and why is it used in Machine Learning? Cross-validation is a technique used to assess the performance and generalization ability of Machine Learning models. The process is repeated multiple times, with each subset serving as both training and testing data.

Data Science 52

Data Science Cross Validation Machine Learning Machine Learning 52

Pickl AI

APRIL 26, 2024

Overfitting occurs when a model learns the training data too well, including noise and irrelevant patterns, leading to poor performance on unseen data. Techniques such as cross-validation, regularisation , and feature selection can prevent overfitting. In my previous role, we had a project with a tight deadline.

Data Analyst 52

Data Analyst Data Analysis Data Analysis Machine Learning 52

Mlearning.ai

FEBRUARY 15, 2024

Data Science Project — Predictive Modeling on Biological Data Part III — A step-by-step guide on how to design a ML modeling pipeline with scikit-learn Functions. Photo by Unsplash Earlier we saw how to collect the data and how to perform exploratory data analysis. Now comes the exciting part ….

Data Science 52

Data Science Decision Trees Exploratory Data Analysis Cross Validation 52

DrivenData Labs

APRIL 10, 2024

Model architectures : All four winners created ensembles of deep learning models and relied on some combination of UNet, ConvNext, and SWIN architectures. In the modeling phase, XGBoost predictions serve as features for subsequent deep learning models. Test-time augmentations were used with mixed results.

Deep Learning 130

Deep Learning Deep Learning Machine Learning Machine Learning 130

PyImageSearch

JANUARY 23, 2023

In this tutorial, you will learn the magic behind the critically acclaimed algorithm: XGBoost. We have used packages like XGBoost, pandas, numpy, matplotlib, and a few packages from scikit-learn. Applying XGBoost to Our Dataset Next, we will do some exploratory data analysis and prepare the data for feeding the model.

Deep Learning 107

Deep Learning Deep Learning Algorithm Decision Trees 107