Search results for "language models"

Natural Language Processing with Deep Learning

Review of Stanford Course on Deep Learning for Natural Language Processing

Natural Language Processing, or NLP, is a subfield of machine learning concerned with understanding speech and text data. Statistical methods and statistical machine learning dominate the field and more recently deep learning methods have proven very effective in challenging NLP problems like speech recognition and text translation. In this post, you will discover the Stanford […]

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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 […]

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What is Teacher Forcing for Recurrent Neural Networks?

What is Teacher Forcing for Recurrent Neural Networks?

Teacher forcing is a method for quickly and efficiently training recurrent neural network models that use the ground truth from a prior time step as input. It is a network training method critical to the development of deep learning language models used in machine translation, text summarization, and image captioning, among many other applications. In […]

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Implementation Patterns for the Encoder-Decoder RNN Architecture with Attention

Implementation Patterns for the Encoder-Decoder RNN Architecture with Attention

The encoder-decoder architecture for recurrent neural networks is proving to be powerful on a host of sequence-to-sequence prediction problems in the field of natural language processing. Attention is a mechanism that addresses a limitation of the encoder-decoder architecture on long sequences, and that in general speeds up the learning and lifts the skill of the […]

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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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Example of LSTMs used in Automatic Handwriting Generation

Gentle Introduction to Generative Long Short-Term Memory Networks

The Long Short-Term Memory recurrent neural network was developed for sequence prediction. In addition to sequence prediction problems. LSTMs can also be used as a generative model In this post, you will discover how LSTMs can be used as generative models. After completing this post, you will know: About generative models, with a focus on […]

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Convolutional Neural Network Long Short-Term Memory Networks

CNN Long Short-Term Memory Networks

Gentle introduction to CNN LSTM recurrent neural networks with example Python code. Input with spatial structure, like images, cannot be modeled easily with the standard Vanilla LSTM. The CNN Long Short-Term Memory Network or CNN LSTM for short is an LSTM architecture specifically designed for sequence prediction problems with spatial inputs, like images or videos. […]

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Automatic Object Detection

8 Inspirational Applications of Deep Learning

It is hyperbole to say deep learning is achieving state-of-the-art results across a range of difficult problem domains. A fact, but also hyperbole. There is a lot of excitement around artificial intelligence, machine learning and deep learning at the moment. It is also an amazing opportunity to get on on the ground floor of some really powerful tech. […]

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