MNIST Classification using CNN | Machine Learning Training Beginner @DataScienceByHenryHarvin
The MNIST handwritten digit classification problem is a standard dataset used in computer vision and deep learning.
Although the dataset is effectively solved, it can be used as the basis for learning and practicing how to develop, evaluate, and use convolutional deep learning neural networks for image classification from scratch. This includes how to develop a robust test harness for estimating the performance of the model, how to explore improvements to the model, and how to save the model and later load it to make predictions on new data
Henry Harvin® ranks amongst the Top 500 Global Ed-tech Companies with 4,60,000+ Alumni,900+ B2B Clients, 500+ Award Winning Trainers, and 1200+ Courses.
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The MNIST handwritten digit classification problem is a standard dataset used in computer vision and deep learning.
Although the dataset is effectively solved, it can be used as the basis for learning and practicing how to develop, evaluate, and use convolutional deep learning neural networks for image classification from scratch. This includes how to develop a robust test harness for estimating the performance of the model, how to explore improvements to the model, and how to save the model and later load it to make predictions on new data
Henry Harvin® ranks amongst the Top 500 Global Ed-tech Companies with 4,60,000+ Alumni,900+ B2B Clients, 500+ Award Winning Trainers, and 1200+ Courses.
Click for More: https://www.henryharvin.com/artificial-intelligence-and-machine-learning-in-business-courseutm_source=youtube&utm_medium=organic
Follow us on
Instagram: https://www.instagram.com/henry_harvin_education/
Facebook: https://www.facebook.com/HenryHarvinEducation/
X: https://twitter.com/henry_harvin_
LinkedIn- https://www.linkedin.com/school/henry-harvin-education/
