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Adversarial Learning with Keras and TensorFlow (Part 2): Implementing the Neural Structured Learning (NSL) Framework and Building a Data Pipeline

PyImageSearch

Home Table of Contents Adversarial Learning with Keras and TensorFlow (Part 2): Implementing the Neural Structured Learning (NSL) Framework and Building a Data Pipeline Adversarial Learning with NSL CIFAR-10 Dataset Configuring Your Development Environment Need Help Configuring Your Development Environment?

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Supercharging Your Data Pipeline with Apache Airflow (Part 2)

Heartbeat

Image Source —  Pixel Production Inc In the previous article, you were introduced to the intricacies of data pipelines, including the two major types of existing data pipelines. You might be curious how a simple tool like Apache Airflow can be powerful for managing complex data pipelines.

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Super charge your LLMs with RAG at scale using AWS Glue for Apache Spark

AWS Machine Learning Blog

Large language models (LLMs) are very large deep-learning models that are pre-trained on vast amounts of data. Data pipelines must seamlessly integrate new data at scale. Diverse data amplifies the need for customizable cleaning and transformation logic to handle the quirks of different sources.

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The 2021 Executive Guide To Data Science and AI

Applied Data Science

This post is a bitesize walk-through of the 2021 Executive Guide to Data Science and AI  — a white paper packed with up-to-date advice for any CIO or CDO looking to deliver real value through data. Download the free, unabridged version here. Machine learning The 6 key trends you need to know in 2021 ?

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Training and Making Predictions with Siamese Networks and Triplet Loss

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Jump Right To The Downloads Section Training and Making Predictions with Siamese Networks and Triplet Loss In the second part of this series, we developed the modules required to build the data pipeline for our face recognition application. Figure 1: Overview of our Face Recognition Pipeline (source: image by the author).

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Triplet Loss with Keras and TensorFlow

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In the previous tutorial of this series, we built the dataset and data pipeline for our Siamese Network based Face Recognition application. Specifically, we looked at an overview of triplet loss and discussed what kind of data samples are required to train our model with the triplet loss. That’s not the case.

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Building a Dataset for Triplet Loss with Keras and TensorFlow

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Project Structure Creating Our Configuration File Creating Our Data Pipeline Preprocessing Faces: Detection and Cropping Summary Citation Information Building a Dataset for Triplet Loss with Keras and TensorFlow In today’s tutorial, we will take the first step toward building our real-time face recognition application. The crop_faces.py