TensorFlow Computer Vision Kaggle Competition MNIST Dual Preprocessing
Template and Solution workbook at DataSImple.education
https://www.datasimple.education/post...
unlock the secrets hidden within the MNIST digits dataset and emerge as a proficient image classifier!
Our CNN block comprises two convolutional layers, a max pooling layer, and a dropout layer. The convolutional layers extract essential features from the input images, while the max pooling layer reduces spatial dimensions. The dropout layer prevents overfitting by randomly deactivating neurons during training, enhancing the model's generalization ability. Together, these components form a powerful architecture that enables accurate classification in the Kaggle competition using the MNIST digits dataset.
What sets this TensorFlow project apart is its unique approach of defining a Python class to enable experimentation. By encapsulating our code and functionality within a class, we create a modular and flexible framework for conducting experiments and fine-tuning our models. This class will serve as a powerful tool that allows us to easily modify various aspects of our model, such as the architecture, hyperparameters, and data preprocessing techniques, all while maintaining a structured and organized codebase.
Python Basics
https://www.datasimple.education/pyth...
check out more data learning videos
https://www.datasimple.education/data...
One on one time with Data Science Teacher Brandyn
https://www.datasimple.education/one-...
data science teacher brandyn on facebook
/ datascienceteacherbrandyn
data science teacher brandyn on linkedin
/ admin
Showcase your DataArt linkedin
/ 1038628576726134
Showcase your DataArt facebook
/ 12736236
Python data analysis group, share your analysis
/ 1531938470572261
Machine learning in sklearn group
/ 575574217682061
Join the deep learning with tensorflow for more info
/ 369278408349330
