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Top NLP Skills, Frameworks, Platforms, and Languages for 2023

ODSC - Open Data Science

Developing NLP tools isn’t so straightforward, and requires a lot of background knowledge in machine & deep learning, among others. In a change from last year, there’s also a higher demand for those with data analysis skills as well. Having mastery of these two will prove that you know data science and in turn, NLP.

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The Data Dilemma: Exploring the Key Differences Between Data Science and Data Engineering

Pickl AI

Data engineers are essential professionals responsible for designing, constructing, and maintaining an organization’s data infrastructure. They create data pipelines, ETL processes, and databases to facilitate smooth data flow and storage. Data Visualization: Matplotlib, Seaborn, Tableau, etc.

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How to become an AI Architect?

Pickl AI

The salary of an Artificial Intelligence Architect in India ranges between ₹ 18.0 An AI Architect is a skilled professional responsible for designing and implementing artificial intelligence solutions within an organization. from 2023 to 2030. Lakhs to ₹ 56.7 Their average annual salary is ₹ 31.8 Who is an AI Architect?

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40 Must-Know Data Science Skills and Frameworks for 2023

ODSC - Open Data Science

Just as a writer needs to know core skills like sentence structure, grammar, and so on, data scientists at all levels should know core data science skills like programming, computer science, algorithms, and so on. Research Why should a data scientist need to have research skills, even outside of academia you ask?

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Training and Making Predictions with Siamese Networks and Triplet Loss

PyImageSearch

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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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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Building a Dataset for Triplet Loss with Keras and TensorFlow

Flipboard

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