Search results for "embedding"

What Are Word Embeddings for Text?

What Are Word Embeddings for Text?

Word embeddings are a type of word representation that allows words with similar meaning to have a similar representation. They are a distributed representation for text that is perhaps one of the key breakthroughs for the impressive performance of deep learning methods on challenging natural language processing problems. In this post, you will discover the […]

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Scatter Plot of PCA Projection of Word2Vec Model

How to Develop Word Embeddings in Python with Gensim

Word embeddings are a modern approach for representing text in natural language processing. Word embedding algorithms like word2vec and GloVe are key to the state-of-the-art results achieved by neural network models on natural language processing problems like machine translation. In this tutorial, you will discover how to train and load word embedding models for natural […]

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How to Use Word Embedding Layers for Deep Learning with Keras

How to Use Word Embedding Layers for Deep Learning with Keras

Word embeddings provide a dense representation of words and their relative meanings. They are an improvement over sparse representations used in simpler bag of word model representations. Word embeddings can be learned from text data and reused among projects. They can also be learned as part of fitting a neural network on text data. In this […]

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Dimensionality Reduction Algorithms With Python

6 Dimensionality Reduction Algorithms With Python

Dimensionality reduction is an unsupervised learning technique. Nevertheless, it can be used as a data transform pre-processing step for machine learning algorithms on classification and regression predictive modeling datasets with supervised learning algorithms. There are many dimensionality reduction algorithms to choose from and no single best algorithm for all cases. Instead, it is a good […]

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A Gentle Introduction to Dimensionality Reduction for Machine Learning

Introduction to Dimensionality Reduction for Machine Learning

The number of input variables or features for a dataset is referred to as its dimensionality. Dimensionality reduction refers to techniques that reduce the number of input variables in a dataset. More input features often make a predictive modeling task more challenging to model, more generally referred to as the curse of dimensionality. High-dimensionality statistics […]

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Examples of Class Leakage in an Image Generated by Partially Trained BigGAN

A Gentle Introduction to BigGAN the Big Generative Adversarial Network

Generative Adversarial Networks, or GANs, are perhaps the most effective generative model for image synthesis. Nevertheless, they are typically restricted to generating small images and the training process remains fragile, dependent upon specific augmentations and hyperparameters in order to achieve good results. The BigGAN is an approach to pull together a suite of recent best […]

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Example of 100 Photos of Sneakers Generated by an AC-GAN

How to Develop an Auxiliary Classifier GAN (AC-GAN) From Scratch with Keras

Generative Adversarial Networks, or GANs, are an architecture for training generative models, such as deep convolutional neural networks for generating images. The conditional generative adversarial network, or cGAN for short, is a type of GAN that involves the conditional generation of images by a generator model. Image generation can be conditional on a class label, […]

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