Image Recognition in Python with TensorFlow and Keras
tutorials/keras/basic_classificationThis article explains how to build a basic image classification model using the Keras library in TensorFlow.
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tutorials/images/transfer_learning
Transfer learning is a technique that allows people to use a pre-trained model for their own purposes. This technique helps people save time and money by not having to train their own model from scratch. Transfer learning works by taking a model that has already been trained on a large dataset and using it to train on a smaller dataset. This allows the model to learn from the larger dataset and then apply that knowledge to the smaller dataset.
Transfer learning is used in many different areas, including computer vision and natural language processing. It is especially useful when there is not enough data available to train a model from scratch. In these cases, transfer learning can be used to quickly get a model up and running.
In this tutorial, we will show how to use transfer learning to classify images of cats and dogs. We will use a pre-trained model that has already been trained on a large dataset of images. We will then use this model to classify images of cats and dogs.
Transfer learning is a powerful technique that can be used to quickly train models on small datasets. It is especially useful when there is not enough data available to train a model from scratch. By using a pre-trained model, we can save time and money while still getting good results.
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