Difference Between Return Sequences and Return States for LSTMs in Keras

Last Updated on August 14, 2019 The Keras deep learning library provides an implementation of the Long Short-Term Memory, or LSTM, recurrent neural network. As part of this implementation, the Keras API provides access to both return sequences and return state. The use and difference between these data can be confusing when designing sophisticated recurrent neural network models, such as the encoder-decoder model. In this tutorial, you will discover the difference and result of return sequences and return states for […]

September 29, 2020 Machine Learning

How to Index, Slice and Reshape NumPy Arrays for Machine Learning

Last Updated on June 13, 2020 Machine learning data is represented as arrays. In Python, data is almost universally represented as NumPy arrays. If you are new to Python, you may be confused by some of the pythonic ways of accessing data, such as negative indexing and array slicing. In this tutorial, you will discover how to manipulate and access your data correctly in NumPy arrays. After completing this tutorial, you will know: How to convert your list data to […]

September 29, 2020 Machine Learning

How to Develop a Seq2Seq Model for Neural Machine Translation in Keras

Last Updated on August 7, 2019 The encoder-decoder model provides a pattern for using recurrent neural networks to address challenging sequence-to-sequence prediction problems, such as machine translation. Encoder-decoder models can be developed in the Keras Python deep learning library and an example of a neural machine translation system developed with this model has been described on the Keras blog, with sample code distributed with the Keras project. In this post, you will discover how to define an encoder-decoder sequence-to-sequence prediction […]

September 29, 2020 Machine Learning

How to Use the Keras Functional API for Deep Learning

Last Updated on May 28, 2020 The Keras Python library makes creating deep learning models fast and easy. The sequential API allows you to create models layer-by-layer for most problems. It is limited in that it does not allow you to create models that share layers or have multiple inputs or outputs. The functional API in Keras is an alternate way of creating models that offers a lot more flexibility, including creating more complex models. In this tutorial, you will […]

September 29, 2020 Machine Learning

Deep Convolutional Neural Network for Sentiment Analysis (Text Classification)

Last Updated on September 3, 2020 Develop a Deep Learning Model to Automatically Classify Movie Reviewsas Positive or Negative in Python with Keras, Step-by-Step. Word embeddings are a technique for representing text where different words with similar meaning have a similar real-valued vector representation. They are a key breakthrough that has led to great performance of neural network models on a suite of challenging natural language processing problems. In this tutorial, you will discover how to develop word embedding models […]

September 29, 2020 Machine Learning

Gentle Introduction to Global Attention for Encoder-Decoder Recurrent Neural Networks

Last Updated on August 14, 2019 The encoder-decoder model provides a pattern for using recurrent neural networks to address challenging sequence-to-sequence prediction problems such as machine translation. Attention is an extension to the encoder-decoder model that improves the performance of the approach on longer sequences. Global attention is a simplification of attention that may be easier to implement in declarative deep learning libraries like Keras and may achieve better results than the classic attention mechanism. In this post, you will […]

September 29, 2020 Machine Learning

Gentle Introduction to Statistical Language Modeling and Neural Language Models

Last Updated on August 7, 2019 Language modeling is central to many important natural language processing tasks. Recently, neural-network-based language models have demonstrated better performance than classical methods both standalone and as part of more challenging natural language processing tasks. In this post, you will discover language modeling for natural language processing. After reading this post, you will know: Why language modeling is critical to addressing tasks in natural language processing. What a language model is and some examples of […]

September 29, 2020 Machine Learning

How to Develop an Encoder-Decoder Model for Sequence-to-Sequence Prediction in Keras

Last Updated on August 27, 2020 The encoder-decoder model provides a pattern for using recurrent neural networks to address challenging sequence-to-sequence prediction problems such as machine translation. Encoder-decoder models can be developed in the Keras Python deep learning library and an example of a neural machine translation system developed with this model has been described on the Keras blog, with sample code distributed with the Keras project. This example can provide the basis for developing encoder-decoder LSTM models for your […]

September 29, 2020 Machine Learning

How to Develop Word-Based Neural Language Models in Python with Keras

Last Updated on September 3, 2020 Language modeling involves predicting the next word in a sequence given the sequence of words already present. A language model is a key element in many natural language processing models such as machine translation and speech recognition. The choice of how the language model is framed must match how the language model is intended to be used. In this tutorial, you will discover how the framing of a language model affects the skill of […]

September 29, 2020 Machine Learning

How to Develop a Character-Based Neural Language Model in Keras

Last Updated on September 3, 2020 A language model predicts the next word in the sequence based on the specific words that have come before it in the sequence. It is also possible to develop language models at the character level using neural networks. The benefit of character-based language models is their small vocabulary and flexibility in handling any words, punctuation, and other document structure. This comes at the cost of requiring larger models that are slower to train. Nevertheless, […]

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