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

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 96

Deep Learning Deep Learning Algorithm Decision Trees 96

Pickl AI

JULY 12, 2024

Summary: This guide explores Artificial Intelligence Using Python, from essential libraries like NumPy and Pandas to advanced techniques in machine learning and deep learning. TensorFlow and Keras: TensorFlow is an open-source platform for machine learning.

Artificial Intelligence 52

Artificial Intelligence Artificial Intelligence Python Natural Language Processing 52

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

DECEMBER 16, 2024

Making Data Stationary: Many forecasting models assume stationarity. If the data is non-stationary, apply transformations like differencing or logarithmic scaling to stabilize its statistical properties. Exploratory Data Analysis (EDA): Conduct EDA to identify trends, seasonal patterns, and correlations within the dataset.

AI 52

AI AI Machine Learning Machine Learning 52