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 […]
Natural Language Processing, or NLP for short, is the study of computational methods for working with speech and text data. The field is dominated by the statistical paradigm and machine learning methods are used for developing predictive models. In this post, you will discover the top books that you can read to get started with […]
Sequence prediction is different from other types of supervised learning problems. The sequence imposes an order on the observations that must be preserved when training models and making predictions. Generally, prediction problems that involve sequence data are referred to as sequence prediction problems, although there are a suite of problems that differ based on the […]
Gentle introduction to the Encoder-Decoder LSTMs for sequence-to-sequence prediction with example Python code. The Encoder-Decoder LSTM is a recurrent neural network designed to address sequence-to-sequence problems, sometimes called seq2seq. Sequence-to-sequence prediction problems are challenging because the number of items in the input and output sequences can vary. For example, text translation and learning to execute […]
Long Short-Term Memory (LSTM) recurrent neural networks are one of the most interesting types of deep learning at the moment. They have been used to demonstrate world-class results in complex problem domains such as language translation, automatic image captioning, and text generation. LSTMs are different to multilayer Perceptrons and convolutional neural networks in that they […]
The amount of data you need depends both on the complexity of your problem and on the complexity of your chosen algorithm. This is a fact, but does not help you if you are at the pointy end of a machine learning project. A common question I get asked is: How much data do I […]
Sequence prediction is a problem that involves using historical sequence information to predict the next value or values in the sequence. The sequence may be symbols like letters in a sentence or real values like those in a time series of prices. Sequence prediction may be easiest to understand in the context of time series […]
Recurrent neural networks, or RNNs, are a type of artificial neural network that add additional weights to the network to create cycles in the network graph in an effort to maintain an internal state. The promise of adding state to neural networks is that they will be able to explicitly learn and exploit context in […]
The Encoder-Decoder architecture is popular because it has demonstrated state-of-the-art results across a range of domains. A limitation of the architecture is that it encodes the input sequence to a fixed length internal representation. This imposes limits on the length of input sequences that can be reasonably learned and results in worse performance for very […]
Long Short-Term Memory or LSTM recurrent neural networks are capable of learning and remembering over long sequences of inputs. LSTMs work very well if your problem has one output for every input, like time series forecasting or text translation. But LSTMs can be challenging to use when you have very long input sequences and only […]
