Recent developments in artificial intelligence have ushered in an era of big-data deep learning. Companies are investing large sums of money in developing software that can perform complex tasks, such as analyzing photos, speech, and video footage to determine if something is suspicious or not.
By taking advantage of parallel computing architectures and vast amounts of data, these AI systems are able to achieve higher accuracy than ever before. Overwhelmingly sized datasets allow computers to find patterns that would be impossible to detect with smaller sets of information.
In this article, you’ll learn how to prepare text data for use with Keras, one of the most popular open source neural network frameworks for computer vision and natural language processing (NLP). You will also learn about some common types of text files and what tools you can use to manipulate them more easily.
## What Is Machine Learning?
A few years ago, there was a lot of talk about _machine learning_. People were talking about it openly and everywhere you looked people were applying machine learning to solve new problems.
Nowadays, **it has become the dominant paradigm for solving complicated computational challenges**.
Organize the data
The next step in preparing your text data is organizing it! Depending on how you gathered your data, you may need to do this already. If you are using pre-existing datasets or sites that contain raw content, you can organize those materials yourself.
For example, if you have several pages of an article, you can combine all these pages into one long string of text. Then, you can use natural language processing (NLP) tools to break up the text into individual sentences and paragraphs.
At this stage, you can either manually edit the results or use computer software to perform this process for you. There are many such programs available free or through software libraries like PyPi.
There are even some companies that offer AI-powered editing services where you upload your material and get back edited versions with improved quality. However, you would still need to check them for accuracy and precision before trusting them fully.
Clean the data
A very common task in almost every field is cleaning or preparing your dataset before you start working with it. This includes both pre-processing steps and what kind of features you want to include in your model!
In this case, we will be doing some basic data cleansing by removing special characters, lower casing letters, and making sure each word is an individual piece of text.
Removing special characters like punctuation can make certain words misbehave because they cannot read them properly. For example, if your model predicts that “dog” is an animal then it may learn that since “cat” has a punctuation mark after it, then also must be an animal so it goes ahead and labels everything as such!
This does not make much sense though because most animals do not have a period at the end! Removing these punctuation marks could potentially break down the classification completely. You can use the Google Chrome extension The Best Extension Collection To Boost Your Productivity Online to easily remove non-alphanumeric symbols from documents, images, videos, and more.
For other changes, you can try using pandas to drop unnecessary columns or rows, delete blank cells, add missing values, etc.
Convert the data to the correct shape
In our first tip, we will go through how to prepare text data in keras for training. This includes changing the shapes of your datasets and how you convert strings into lists or other shapes.
Many natural language processing (NLP) applications require large volumes of structured data that have features and labels. These apps then use neural networks to process the data and achieve better results than traditional machine learning algorithms like logistic regression.
Neural networks are a type of algorithm that work by having several layers that connect together. The layers can be perception-related such as image recognition or feature extraction, or execution-related, such as regrouping information for inference.
In this article, we will discuss some ways to prepare textual data for neural network training with keras. You will learn about different types of texts, encoding them, and converting between string representations and list structures.
Choose your dataset and learning model
Choosing how to prepare text data is an important part of developing models using keras. You will need to decide what type of features you want to learn and what domain the feature set should be applicable in.
There are two main types of features that can be used when training neural networks, numeric or categorical. Numeric features are easy to define, but may not give very meaningful results depending on how they are applied. For example, if we wanted to predict whether something is positive or negative sentiment about a product, knowing the average price of the item would not necessarily tell us much!
Categorical features work better than numerical ones in this case because they relate directly to concepts such as products or emotions. The most common way to create categorical features is by defining words and creating one feature per word.
For our purposes here, we will focus only on creating numeric features. This article will go into more detail on how to do this, so keep reading!
Numerical features are great for applications where you know there will be discrete numbers associated with each piece of data. An example of this could be predicting salary for employees or determining credit risk for loans. Because these predictions are clearly defined, it is possible to calculate some metrics like mean and variance.
Another popular kind of numerical feature is tf-idf (term frequency – inverse document frequency).
Use a pretrained model
A pretrained model is a neural network that has been trained on large datasets using a method called transfer learning. This allows you to use the net as a starting point and then tune it to your needs, just like how you would train yourself!
Pretrained models are very common these days because they have become almost standard in most areas of technology. Google uses them extensively in their AI products, making them an excellent option if you want to learn more about deep learning.
There are many different types of pretrained networks available from various sources. Some focus on image classification while others do natural language processing (NLP). Finding one that fits your project’s goals is easy since most come with code that can be adapted or used directly.
This article will go into detail on how to prepare text data for keras using two different pretrained sequences to show off some capabilities of the tool.
Adjust the learning model
When working with text data, one of the first steps is to prepare your data in the proper format. This includes changing the casing style, splitting up sentences into individual words or phrases, and converting special characters into plain old letters.
After that, you will need to choose which vectorization method is best for your dataset. Some common methods include tokenizing the data, embedding the tokens as vectors, and using pretrained word vectors to do more advanced processing like clustering or classification.
You can also use sequence models such as LSTMs or GRUs to process the information sequentially. All of these can be implemented in Keras!
Summary
At the end of this article, you should know how to work with textual data in Keras by practicing around half a dozen different tasks. You are also familiar with some helpful tools for altering and preparing the data before training.
Create a new learning model
A deep neural network is an interesting structure that involves stacking multiple layers of neurons (think about it like ladders with steps) to perform specific tasks. When you are using a DNN for classification, the final layer will identify which category each item in your dataset belongs to.
When working with textual data, such as tweets or blogs, one can use pretrained word embeddings to help the computer understand the meaning of individual words. These pre-existing dictionaries also give numerical representations of terms to aid in computationalization.
There are many excellent pretrained models available online that have people’s hard work uploaded automatically.
Modify the learning model
A common way to use neural networks is as a classifier. This means that you can use the trained network to determine which category or group an item belongs to. For example, you could have it identify whether an image contains a dog or not, or classify what genre of movie a clip features in.
When using a neural network as a classification tool, there are two main components of the algorithm that need to be adjusted before training starts. The first is how many layers the network has, and the second is what kind of activation function each layer uses.
This article will go into detail about both of these and how to choose them for your own text data set. Then, we’ll see some examples of different architectures used for textual deep learning with Keras.
