PyImageSearch

JANUARY 15, 2024

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? We open our config.py

Data Pipeline 67

Data Pipeline Deep Learning Deep Learning Computer Science 67

Flipboard

FEBRUARY 13, 2023

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 dataset.py

Data Pipeline 142

Data Pipeline Deep Learning Deep Learning Python 142

PyImageSearch

MARCH 20, 2023

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).

Deep Learning 119

Deep Learning Deep Learning Data Pipeline Python 119

Flipboard

MARCH 6, 2023

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. Download the code!

Deep Learning 142

Deep Learning Deep Learning Data Pipeline Computer Science 142

Flipboard

NOVEMBER 24, 2023

Iris was designed to use machine learning (ML) algorithms to predict the next steps in building a data pipeline. About the Authors Greg Benson is a Professor of Computer Science at the University of San Francisco and Chief Scientist at SnapLogic. He is the author of the upcoming book “What’s Your Problem?”

Database 156

Database AWS ETL SQL 156

PyImageSearch

JULY 24, 2023

Going Beyond with Keras Core The Power of Keras Core: Expanding Your Deep Learning Horizons Show Me Some Code JAX Harnessing model.fit() Imports and Setup Data Pipeline Build a Custom Model Build the Image Classification Model Train the Model Evaluation Summary References Citation Information What Is Keras Core? Enter Keras Core!

Deep Learning 98

Deep Learning Deep Learning Machine Learning Machine Learning 98

AWS Machine Learning Blog

SEPTEMBER 11, 2024

In computer science, a number can be represented with different levels of precision, such as double precision (FP64), single precision (FP32), and half-precision (FP16). To give an idea of scale, the largest financial data feed is the consolidated US equity options feed, termed OPRA.

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AWS ML ML Clustering 113

PyImageSearch

JUNE 5, 2023

Specifically, we will develop our data pipeline, implement the loss functions discussed in Part 1 and write our own code to train the CycleGAN model end-to-end using Keras and TensorFlow. Finally, we combine and consolidate our entire training data (i.e., Or requires a degree in computer science?

Deep Learning 57

Deep Learning Deep Learning Data Pipeline Python 57

PyImageSearch

JANUARY 8, 2024

We will understand the dataset and the data pipeline for our application and discuss the salient features of the NSL framework in detail. config.py ) The data pipeline (i.e., Do you think learning computer vision and deep learning has to be time-consuming, overwhelming, and complicated? That’s not the case.

Deep Learning 67

Deep Learning Deep Learning Data Pipeline Computer Science 67