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

DagsHub

APRIL 21, 2024

Source: [link] Similarly, while building any machine learning-based product or service, training and evaluating the model on a few real-world samples does not necessarily mean the end of your responsibilities. MLOps tools play a pivotal role in every stage of the machine learning lifecycle. What is MLOps?

Machine Learning 59

Machine Learning Machine Learning ML ML 59

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

Mlearning.ai

MAY 23, 2023

Read the full blog here —  [link] Data Science Interview Questions for Freshers 1. What is Data Science? The following Venn diagram depicts the difference between data science and data analytics clearly: 3. Data analysis can not be done on a whole volume of data at a time especially when it involves larger datasets.

Data Science 52

Data Science Decision Trees Deep Learning Deep Learning 52

Heartbeat

MAY 29, 2023

In this article, we will explore the essential steps involved in training LLMs, including data preparation, model selection, hyperparameter tuning, and fine-tuning. We will also discuss best practices for training LLMs, such as using transfer learning, data augmentation, and ensembling methods.

Machine Learning 78

Machine Learning Machine Learning Natural Language Processing Data Preparation 78

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

DataRobot Blog

DECEMBER 20, 2022

DataRobot combines these datasets and data types into one training dataset used to build machine learning models. You can understand the data and model’s behavior at any time. This would entail a roughly +/-€24,520 price difference on average, compared to the true price, using MAE (Mean Absolute Error) Cross Validation.

AI 59

AI AI Cross Validation Machine Learning 59

Mlearning.ai

JUNE 27, 2023

To address such tasks and uncover behavioral patterns, we turn to a powerful technique in Machine Learning called Clustering. Originally used in Data Mining, clustering can also serve as a crucial preprocessing step in various Machine Learning algorithms. How would we tackle this challenge? K = 3 ; 3 Clusters.

Clustering 52

Clustering Algorithm Data Analyst Machine Learning 52

Towards AI

AUGUST 11, 2023

Tabular data has been around for decades and is one of the most common data types used in data analysis and machine learning. Traditionally, tabular data has been used for simply organizing and reporting information.

Machine Learning 88

Machine Learning Machine Learning AI AI 88