Input Methods, String & Unicode, Regular Expression Use Cases — NLTK has preprocessed texts. But we can also import and process our own texts. Importing from __future__ import division import nltk, re, pprint To Import a Book as a Txt Install urlopen: !pip install urlopen And: import urllib.request url = "https://www.gutenberg.org/files/11/11-0.txt" raw = urllib.request.urlopen(url).read() type(raw) # <type 'str'>

Grammars, Derivation, Expressiveness, Chomsky Hierarchy — 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…

Frequency Distributions, Wordlists, WordNet, Semantic Similarity — Work in Natural Language Processing typically uses large bodies of linguistic data. In this article, we explore some lexical resources that help us ingest and analyze corpora. These resources are part of Python or the NLTK library. Getting NLTK Corpora We can access pre-imported corpora in NLTK in one of 2 ways: emma…

LSTM, Sampling, Smart Code Completion Tool — How Do You Generate Sequence Data? 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…

Bidirectional RNNs, Encoding, Word Embedding and Tips — What's a Bidirectional RNN? 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…

Recurrent Layers: SimpleRNN, LSTM, GRU — What’s SimpleRNN? 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. …

Easing Into Recurrent Neural Networks — Remember these two useful properties of Convolutional Models. Convolutional Models Overview Convolutions, Kernels, Downsampling & Propertiesmedium.com Translation Invariance A convolutional model can learn a certain pattern in the lower right area, then after that point detect it anywhere on the image. Spatial Hierarchy A convolutional model can learn patterns in a hierarchical fashion, much like we do. The…

Creating, Preprocessing, Data Augmentation, Feature Extraction, Fine Tuning — How to make your own Convolutional Model?

Convolutions, Kernels, Downsampling & Properties — What does a CNN model look like in code? 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.MaxPooling2D((2, 2))) 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'))