Search results for "embedding"

Photo of a dog at the beach.

How to Develop a Deep Learning Photo Caption Generator from Scratch

Develop a Deep Learning Model to Automatically Describe Photographs in Python with Keras, Step-by-Step. Caption generation is a challenging artificial intelligence problem where a textual description must be generated for a given photograph. It requires both methods from computer vision to understand the content of the image and a language model from the field of […]

Continue Reading 1,187
One-Shot Learning with Siamese Networks, Contrastive, and Triplet Loss for Face Recognition

One-Shot Learning for Face Recognition

One-shot learning is a classification task where one, or a few, examples are used to classify many new examples in the future. This characterizes tasks seen in the field of face recognition, such as face identification and face verification, where people must be classified correctly with different facial expressions, lighting conditions, accessories, and hairstyles given […]

Continue Reading 31
Detected Face of Jerry Seinfeld, Correctly Identified by the SVM Classifier

How to Develop a Face Recognition System Using FaceNet in Keras

Face recognition is a computer vision task of identifying and verifying a person based on a photograph of their face. FaceNet is a face recognition system developed in 2015 by researchers at Google that achieved then state-of-the-art results on a range of face recognition benchmark datasets. The FaceNet system can be used broadly thanks to […]

Continue Reading 558
Face Detected From a Photograph of Sharon Stone Using an MTCNN Model

How to Perform Face Recognition With VGGFace2 in Keras

Face recognition is a computer vision task of identifying and verifying a person based on a photograph of their face. Recently, deep learning convolutional neural networks have surpassed classical methods and are achieving state-of-the-art results on standard face recognition datasets. One example of a state-of-the-art model is the VGGFace and VGGFace2 model developed by researchers […]

Continue Reading 145
Practical Recommendations for Deep Learning Neural Network Practitioners

Recommendations for Deep Learning Neural Network Practitioners

Deep learning neural networks are relatively straightforward to define and train given the wide adoption of open source libraries. Nevertheless, neural networks remain challenging to configure and train. In his 2012 paper titled “Practical Recommendations for Gradient-Based Training of Deep Architectures” published as a preprint and a chapter of the popular 2012 book “Neural Networks: […]

Continue Reading 8
Line Plot for Supervised Greedy Layer-Wise Pretraining Showing Model Layers vs Train and Test Set Classification Accuracy on the Blobs Classification Problem

How to Use Greedy Layer-Wise Pretraining in Deep Learning Neural Networks

Training deep neural networks was traditionally challenging as the vanishing gradient meant that weights in layers close to the input layer were not updated in response to errors calculated on the training dataset. An innovation and important milestone in the field of deep learning was greedy layer-wise pretraining that allowed very deep neural networks to […]

Continue Reading 57
Overview of Course Structure

Practical Deep Learning for Coders (Review)

Practical deep learning is a challenging subject in which to get started. It is often taught in a bottom-up manner, requiring that you first get familiar with linear algebra, calculus, and mathematical optimization before eventually learning the neural network techniques. This can take years, and most of the background theory will not help you to […]

Continue Reading 26