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Build Your First Deep Learning Classifier using TensorFlow: Dog Breed Example

#artificialintelligence

In this article, I will present several techniques for you to make your first steps towards developing an algorithm that could be used for a classic image classification problem: detecting dog breed from an image. By the end of this article, we'll have developed code that will accept any user-supplied image as input and return an estimate of the dog's breed. Also, if a human is detected, the algorithm will provide an estimate of the dog breed that is most resembling. This project was completed as part of Udacity's Machine Learning Nanodegree (GitHub repo). Convolutional neural networks (also refered to as CNN or ConvNet) are a class of deep neural networks that have seen widespread adoption in a number of computer vision and visual imagery applications.


Build Your First Deep Learning Classifier using TensorFlow: Dog Breed Example

#artificialintelligence

In this article, I will present several techniques for you to make your first steps towards developing an algorithm that could be used for a classic image classification problem: detecting dog breed from an image. By the end of this article, we'll have developed code that will accept any user-supplied image as input and return an estimate of the dog's breed. Also, if a human is detected, the algorithm will provide an estimate of the dog breed that is most resembling. This project was completed as part of Udacity's Machine Learning Nanodegree (GitHub repo). Convolutional neural networks (also refered to as CNN or ConvNet) are a class of deep neural networks that have seen widespread adoption in a number of computer vision and visual imagery applications.


ImageNet: VGGNet, ResNet, Inception, and Xception with Keras - PyImageSearch

#artificialintelligence

A few months ago I wrote a tutorial on how to classify images using Convolutional Neural Networks (specifically, VGG16) pre-trained on the ImageNet dataset with Python and the Keras deep learning library. The pre-trained networks inside of Keras are capable of recognizing 1,000 different object categories, similar to objects we encounter in our day-to-day lives with high accuracy. Back then, the pre-trained ImageNet models were separate from the core Keras library, requiring us to clone a free-standing GitHub repo and then manually copy the code into our projects. This solution worked well enough; however, since my original blog post was published, the pre-trained networks (VGG16, VGG19, ResNet50, Inception V3, and Xception) have been fully integrated into the Keras core (no need to clone down a separate repo anymore) -- these implementations can be found inside the applications sub-module. Because of this, I've decided to create a new, updated tutorial that demonstrates how to utilize these state-of-the-art networks in your own classification projects.


Bengali.AI Handwritten Grapheme Classification Model Enhancements

#artificialintelligence

Following up on our initial article where we outlined our baseline model; this article is being produced in order to further document our team's efforts competing in the Kaggle Bengali.AI Handwritten Grapheme Classification competition. In summary, our goal is to classify images of handwritten Bengali characters using a Convolutional Neural Network (CNN). Our initial steps in this process were covered in our initial article linked above. Here we document additional steps we took such as data augmentation, transfer learning, ResNet, and hyperparameter tuning in order to improve upon our baseline model. As mentioned in our last post, we wanted to use data augmentation to expand our training dataset and create a more robust model.


OCR with Keras, TensorFlow, and Deep Learning - PyImageSearch

#artificialintelligence

In this tutorial, you will learn how to train an Optical Character Recognition (OCR) model using Keras, TensorFlow, and Deep Learning. For now, we'll primarily be focusing on how to train a custom Keras/TensorFlow model to recognize alphanumeric characters (i.e., the digits 0-9 and the letters A-Z). Building on today's post, next week we'll learn how we can use this model to correctly classify handwritten characters in custom input images. We'll be starting with the fundamentals of using well-known handwriting datasets and training a ResNet deep learning model on these data. To learn how to train an OCR model with Keras, TensorFlow, and deep learning, just keep reading.