TensorFlow Intermediate: Natural Language Processing

Name: TensorFlow Intermediate: Natural Language Processing
Author: Josh Turner

Master TensorFlow in deep learning with a focus on natural language processing

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Course: On-Demand

Hello, my name is Josh Turner, and in these lessons, you'll learn about language generation with reoccurrent neural networks and I'll show you some advanced NLP techniques and applications. In this lesson, you'll learn an introduction to natural language processing or NLP. We'll go into pre-processing text data and then build a simple NLP model. Let's go ahead and get started. The first thing we need to do is import the necessary libraries. Make sure you have the Python editor of your choosing open and go ahead and pip install these environments. For this lesson, we'll import TensorFlow as TF. And from the tensorflow.kharas.pre-processing.text library, we'll import the tokenizer and from the tensorflow.kharas.pre-processing.sequence library, we'll import pad sequences. These will be useful for pre-processing our text data. Finally, from the tensorflow.kharas.models, we'll import the sequential model and from the layers library, we'll import the embedding LSTM and dense layers. Okay, that was a mouthful. Go ahead and run this cell code to make sure it works on your local machine. All right, great. Now, text pre-processing is kind of special in that it has its own kind of way that it needs to be pre-processed. And I think the best way to go through this and show you is by example. So I'm just going to create some simple text data and show you how it's pre-processed so that it might be used for a machine learning model. Create a list with some strings in them, just some sentences that you want to write out. For me, I've just written hello, how are you? I am doing great. What about you? Next, create a variable called tokenizer. This will create an instance of the tokenizer class. We can fit the tokenizer on text, and this will make our text and create each letter in our text in the tokens. We can then create a variable called sequences and also use as an attribute of the tokenizer the text to sequences. So this variable will create our text and create them in the sequences. After that, the sequences might not be all the same length. So we may have to pad them, and we'll use the padding variable to do that. So create a variable called maxlink, it'll be the max length of your sequences, and then we'll pad all sequences with zeros until they are that maxlink. Okay, go ahead and run this cell of code to see what some of your sequences look like. You can see we have three sequences, all of the same size using this all completely representative of these three sentences that we made in text. So now we're ready, and we have an input for our NLP model. So let's go ahead and build just a simple NLP model. Using the TensorFlow capabilities, we can create a very simple example of the sequential class and add some layers to create an NLP model. To do that, we're going to go ahead and create a variable called model and set that equal to an instance of the sequential class. Using the add attribute, we'll add the embedding layer, which will have input dimension, the length of the tokenizer dot word index plus one. Okay, so the tokenizer is going to tell us what our dimension is. Output dimension could be anything that you want, but the input length must be the length of your sequences, which in our case is max link. To that, we can add another layer using the dot add attribute, we'll add a simple LSTM with 128 nodes in this case. And finally, we'll output everything with a dense layer with just one output to predict whatever the next letter in the sequences and then using a sigmoid activation function. So go ahead and run this cell of code and you will have defined your model. Okay, and then the next thing we want to talk about is actually compiling our model so that it would be ready for training in a further step. So to do that, we can again use the model dot compile attribute. And we'll use the atom optimizer, which is a state of the our optimizer. Since in this case, we're using a sort of binary object here, we'll use binary cross entropy and we will use accuracy as our metric. So you can define this, run this cell of code, make sure your model is compiled, and now it's ready for training and you're ready to go. Stay tuned for the next lesson where I'll show you text pre-processing and tokenization. Thanks for watching.

Intermediate Provider : Josh Turner 5 Lessons · 27m in English

Course Description

In this intermediate-level TensorFlow course, we’ll explore the applications of natural language processing (NLP) and learn to build text classification models, generate language using recurrent neural networks, and apply advanced NLP techniques. We’ll learn about TensorFlow's capabilities for NLP and unlock the potential of deep learning in processing textual data.

NLP tasks can be important in various applications. You’ll explore tasks like sentiment analysis, text classification, and machine translation. Understanding these tasks sets the stage for diving deeper into NLP techniques.

You will delve into text preprocessing and tokenization specifically tailored for NLP. You will learn how to clean and preprocess text data, handle common challenges like noise and inconsistencies, and apply techniques like tokenization and normalization to prepare the data for further analysis.

The course then explores the exciting world of language generation using recurrent neural networks (RNNs). You will learn how to use RNNs to generate text, complete sentences, and even generate coherent paragraphs. By understanding the inner workings of RNNs and their ability to capture sequential dependencies, you’ll see the potential of language generation tasks.

What You'll Learn

Understand the importance of NLP and its applications such as sentiment analysis, text classification, and machine translation
Preprocess and tokenize text data, including cleaning data and handling noise and inconsistencies
Build and train text classification models using TensorFlow
Generate language with recurrent neural networks (RNNs) to complete sentences and produce coherent paragraphs
Apply advanced NLP techniques to improve model performance

Key Takeaways

This intermediate-level TensorFlow course covers building text classification models, generating language with RNNs, and applying advanced NLP techniques.
NLP tasks such as sentiment analysis, text classification, and machine translation are important across various applications.
Text preprocessing and tokenization prepare text data for analysis by cleaning it and handling challenges like noise and inconsistencies.
Recurrent neural networks capture sequential dependencies, enabling language generation tasks like completing sentences and generating paragraphs.
TensorFlow provides capabilities for processing textual data through deep learning.

Frequently Asked Questions

Who is this course for?

This is an intermediate-level TensorFlow course for learners who want to explore natural language processing and apply deep learning to textual data.

What topics does this course cover?

It covers an introduction to NLP, text preprocessing and tokenization, building a text classification model, language generation with recurrent neural networks, and advanced NLP techniques and applications.

What skills will I gain?

You will gain skills in Natural Language Processing, Natural Language Understanding, TensorFlow, and Text Processing.

What NLP tasks are discussed in this course?

The course explores tasks like sentiment analysis, text classification, and machine translation.

What is the role of recurrent neural networks in this course?

RNNs are used for language generation, allowing you to generate text, complete sentences, and generate coherent paragraphs by capturing sequential dependencies.