Monday, March 6, 2023

New Tutorial: Triplet Loss with Keras and TensorFlow

Hi,

This week you'll learn about Triplet Loss with Keras and TensorFlow.

We are excited to announce our latest blog post about Triplet Loss with Keras and TensorFlow! In this lesson, we explore Triplet Loss and how to implement it with Keras and TensorFlow.

In the previous blog post of this series, we learned how to create the ideal dataset required for training a Siamese network. In this blog post, we additionally see how to plug in our dataset-building pipeline for a swift progression!

The big picture: Triplet Loss is a very popular and staple concept in machine learning and has a variety of real-world applications. In this blog post, we explain how Triplet Loss works and dive into the math behind it. Then, we use this information to build a Siamese model with the training and testing pipelines.

How it works: Triplet Loss is a function used to learn a mapping function that can embed images or other data points into a vector space so that similar items are closer together and dissimilar items are farther apart. This task is important for face recognition, image retrieval, and person re-identification.

Our thoughts: As one of the final remaining pieces of this puzzle, understanding Triplet Loss and its incorporation into the Siamese model pipeline is vital in finalizing our face recognition project.

Yes, but: The training and evaluation of our model will be explored in the upcoming blogs of this series.

Stay smart: Remember to experiment with different parameters and optimizers once you are familiar with the training stage to see what works best!

Click here to read the full tutorial

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