Archive | Deep Learning for Computer Vision

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A Gentle Introduction to Channels-First and Channels-Last Image Formats

Color images have height, width, and color channel dimensions. When represented as three-dimensional arrays, the channel dimension for the image data is last by default, but may be moved to be the first dimension, often for performance-tuning reasons. The use of these two “channel ordering formats” and preparing data to meet a specific preferred channel […]

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Example of Displaying a PIL image using the Default Application

How to Load, Convert, and Save Images With the Keras API

The Keras deep learning library provides a sophisticated API for loading, preparing, and augmenting image data. Also included in the API are some undocumented functions that allow you to quickly and easily load, convert, and save image files. These functions can be convenient when getting started on a computer vision deep learning project, allowing you […]

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A Gentle Introduction to the Promise of Deep Learning for Computer Vision

A Gentle Introduction to the Promise of Deep Learning for Computer Vision

The promise of deep learning in the field of computer vision is better performance by models that may require more data but less digital signal processing expertise to train and operate. There is a lot of hype and large claims around deep learning methods, but beyond the hype, deep learning methods are achieving state-of-the-art results […]

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Convolutional Neural Networks Taught by Andrew Ng

DeepLearning.AI Convolutional Neural Networks Course (Review)

Andrew Ng is famous for his Stanford machine learning course provided on Coursera. In 2017, he released a five-part course on deep learning also on Coursera titled “Deep Learning Specialization” that included one module on deep learning for computer vision titled “Convolutional Neural Networks.” This course provides an excellent introduction to deep learning methods for […]

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