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How to Apply K-Fold Averaging on Deep Learning Classifier

Analytics Vidhya

This article was published as a part of the Data Science Blogathon In this article, we will be learning about how to apply k-fold cross-validation to a deep learning image classification model. The post How to Apply K-Fold Averaging on Deep Learning Classifier appeared first on Analytics Vidhya.

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Top 10 Data Science Interviews Questions and Expert Answers

Pickl AI

Data Science interviews are pivotal moments in the career trajectory of any aspiring data scientist. Having the knowledge about the data science interview questions will help you crack the interview. Data Science skills that will help you excel professionally.

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List of Python Libraries for Data Science

Pickl AI

To help you understand Python Libraries better, the blog will explain a Python Libraries for Data Science List which you can learn about. This may include for instance in Machine Learning, Data Science, Data Visualisation, image and Data Manipulation. What is a Python Library?

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Data Science Project?—?Predictive Modeling on Biological Data

Mlearning.ai

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 ….

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Cheat Sheets for Data Scientists – A Comprehensive Guide

Pickl AI

It serves as a handy quick-reference tool to assist data professionals in their work, aiding in data interpretation, modeling , and decision-making processes. In the fast-paced world of Data Science, having quick and easy access to essential information is invaluable when using a repository of Cheat sheets for Data Scientists.

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Are you familiar with the teacher of machine learning?

Dataconomy

Python machine learning packages have emerged as the go-to choice for implementing and working with machine learning algorithms. These libraries, with their rich functionalities and comprehensive toolsets, have become the backbone of data science and machine learning practices.

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Meet the winners of the Mars Spectrometry 2: Gas Chromatography Challenge

DrivenData Labs

The results of this GCMS challenge could not only support NASA scientists to more quickly analyze data, but is also a proof-of-concept of the use of data science and machine learning techniques on complex GCMS data for future missions. All winners who used deep learning fine-tuned pre-trained models.