Recognizing Cats and Dogs With TensorFlow

Recognizing Cats and Dogs With TensorFlow

Create a neural network using TensorFlow to recognize cats and dogs

I like to use practical examples and projects to help me memorize the theory during my study of Deep Neural Networks. An excellent resource for finding these practical projects is Kaggle. Kaggle is an online community of data scientists and machine learning practitioners.

Kaggle allows you to search and publish data sets, explore, and build models. You can perform these functions in a web-based environment. Kaggle also offers machine learning competitions with actual problems and provides prizes to the winners.

I am currently studying Deep Learning with TensorFlow. One of the subjects I want to learn is image recognition. This article describes my attempt to solve a former Kaggle competition from 2013, called “Dogs vs. Cats.” For implementing the solution I used Python 3.8 and TensorFlow 2.3.0.

The original “Dogs vs. Cats” competition’s goal was to write an algorithm to classify whether images contain either a dog or a cat. Note that in 2013 there was no TensorFlow or another framework such as PyTorch to help.

Although the competition is finished, it is still possible to upload and let Kaggle score your predictions.

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