The general way is to train a machine learning model then ask it to predict the next token, whether they be characters or words or n-grams. A model with this predictive capability is called a Language Model. The model is basically learning the latent space i.e. the statistical structure of the given data.
This model will spit out an output based on an input. Then replace the output as the input for another round of text generation. And repeat the process.
More concretely, given a sequence like “Cat in the ha”, the language model would predict, “t”. Assuming the model…
Bidirectional RNN is an RNN variant, that sometimes can increase performance. It is especially useful for natural language processing tasks.
The BD-RNN uses two regular RNNs, one of them where the sequential data is going forward, and one where the data sequences backwards, then merging their representations.
This method doesn’t work very well for timeseries data, since there’s a more abstract meaning to chronological order. For example, it does make sense that more recent events should have more weight in predicting what will happen next.
Whereas in language related problems, its clear that “cat in the hat” and “tah eht…
SimpleRNN is the recurrent layer object in Keras.
from keras.layers import SimpleRNN
Remember that we input our data point, for example the entire length of our review, the number of timesteps.
Now the SimpleRNN processes data in batches, just like every other neural network. So this layer takes as input the tensor (batch_size, timesteps, input_features).
All recurrent layers in keras can be run in two different modes:
Let’s look at writing a simple recurrent model:
from keras.models import Sequential
from keras.layers import Embedding, SimpleRNNmodel = Sequential() model.add(Embedding(10000…
Remember these two useful properties of Convolutional Models.
A convolutional model can learn a certain pattern in the lower right area, then after that point detect it anywhere on the image.
A convolutional model can learn patterns in a hierarchical fashion, much like we do. The first layers will learn relatively simple patterns, like horizontalness and verticalness etc. Then the second layers will put these together to learn such things as corners. And so on with each new layer.
So if we take the translation invariance property and apply it to sequential data, such as text…
from keras import layers
from keras import models seq_model= models.Sequential()
seq_model.add(layers.Conv2D(32, (3, 3), activation='relu', input_shape=(28, 28, 1)))
seq_model.add(layers.Conv2D(64, (3, 3), activation='relu')) seq_model.add(layers.MaxPooling2D((2, 2)))
seq_model.add(layers.Conv2D(128, (3, 3), activation='relu'))
There is a model:
from keras import modelsseq_model= models.Sequential()
Models can be sequential and non-sequential.
from keras.models import Sequential, Modelnon_seq_model = Model(input_tensor, output_tensor)
Models can consist of layers:
from keras import layersseq_model.add(layers.Conv2D(32, (3, 3), activation='relu', input_shape=(28, 28, 1)))
input_shapeis (28, 28, 1), which takes an input tensors of shape (
Many endurance sports require the participant to follow a pre-set course and achieve specific control points by a specific time. Confirmation of a participants adherence to the pre-set course and control guidelines has been accomplished through the use of paper systems, photographs and manned control points.
The use of a participants GPS file, generated from the participants bike computer, sport watch or smartphone is gaining adoption. However that requires the event coordinator to manually gather, compare and manage the participants GPS file for verification purposes. …
Previously, we talked about how languages are studied using the notion of a formal language. Formal language is a mathematical construction that uses sets to describe a language and understand its properties.
We introduced the notion of a string, which is a word or sequence of characters, symbols or letters. Then we formally defined the alphabet, which is a set of symbols. The alphabet often goes hand in hand with the language because we define a formal language as a set of strings over a unique alphabet.
Then we explored…
This article is meant to be a gentle introduction to NLTK. As with everything, we will try to balance mathematical rigor, programmatic ease of use with concrete examples that have linguistically motivated examples.
In many ways, this article is the programmatic introduction to computational linguistics, and is a mirror to this article.
We will try to predict the median house price given 13 different parameters. The parameters are attributes such as crime rate, property tax rate, square footage etc.
The input variables in order are:
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