Captioning an image involves generating a human readable textual description given an image, such as a photograph. It is an easy problem for a human, but very challenging for a machine as it involves both understanding the content of an image and how to translate this understanding into natural language. Recently, deep learning methods have […]
Deep Learning for NLP Crash Course. Bring Deep Learning methods to Your Text Data project in 7 Days. We are awash with text, from books, papers, blogs, tweets, news, and increasingly text from spoken utterances. Working with text is hard as it requires drawing upon knowledge from diverse domains such as linguistics, machine learning, statistical […]
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 […]
Develop a Deep Learning Model to Automatically Classify Movie Reviews as 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 […]
Text classification describes a general class of problems such as predicting the sentiment of tweets and movie reviews, as well as classifying email as spam or not. Deep learning methods are proving very good at text classification, achieving state-of-the-art results on a suite of standard academic benchmark problems. In this post, you will discover some […]
You cannot go straight from raw text to fitting a machine learning or deep learning model. You must clean your text first, which means splitting it into words and handling punctuation and case. In fact, there is a whole suite of text preparation methods that you may need to use, and the choice of methods […]
Text data preparation is different for each problem. Preparation starts with simple steps, like loading data, but quickly gets difficult with cleaning tasks that are very specific to the data you are working with. You need help as to where to begin and what order to work through the steps from raw data to data […]
Deep learning is having a large impact on the field of natural language processing. But, as a beginner, where do you start? Both deep learning and natural language processing are huge fields. What are the salient aspects of each field to focus on and which areas of NLP is deep learning having the most impact? […]
Neural network algorithms are stochastic. This means they make use of randomness, such as initializing to random weights, and in turn the same network trained on the same data can produce different results. This can be confusing to beginners as the algorithm appears unstable, and in fact they are by design. The random initialization allows […]
Sequence classification is a predictive modeling problem where you have some sequence of inputs over space or time, and the task is to predict a category for the sequence. This problem is difficult because the sequences can vary in length, comprise a very large vocabulary of input symbols, and may require the model to learn […]
