Get familiar with the basic machine learning techniques used in natural language processing.

Mastering Natural Language Processing

This module was created by AdaptiLab, a company specializing in evaluating, sourcing, and upskilling enterprise machine-learning talent. It’s built in collaboration with industry machine learning experts from Google, Microsoft, Amazon, and Apple.

The code for this module is built around the TensorFlow framework, one of the premier frameworks for industry machine learning, and the Python pandas library for data analysis. A knowledge of Python and TensorFlow is required for this module.

Natural Language Processing with Machine Learning

<h2 class="section-title">Chapter Goals:</h2>
<ul>
	<li>Learn how to improve the performance of the BiLSTM classifier</li>
</ul>


<h2>A. Model performance</h2>

<div id="section2_l7" class="collapse show">
<p>The current iteration of the model will perform well on relatively simple datasets and can even perform adequately on larger datasets. However, there are still improvements that can be made to the model.</p>
<p>An easy way to improve the model would be to use more LSTM layers or hidden LSTM units. Our model only used a single LSTM layer with 512 hidden units, so adding more LSTM layers may benefit the model's performance. However, similar to adding hidden layers to a feed-forward neural network, it is important to make sure that additional LSTM layers don't lead to the model overfitting the training dataset.</p>
</div>


<h2>Chapter Goals:</h2>
<ul>
	<li>Learn how to improve the performance of the BiLSTM classifier</li>
</ul>


<h2>A. Model performance</h2>

<div id="section2_l7">
<p>The current iteration of the model will perform well on relatively simple datasets and can even perform adequately on larger datasets. However, there are still improvements that can be made to the model.</p>
<p>An easy way to improve the model would be to use more LSTM layers or hidden LSTM units. Our model only used a single LSTM layer with 512 hidden units, so adding more LSTM layers may benefit the model's performance. However, similar to adding hidden layers to a feed-forward neural network, it is important to make sure that additional LSTM layers don't lead to the model overfitting the training dataset.</p>
</div>


Tips and advice on how to improve the BiLSTM model performance.

What you'll learn from this module

Word Embeddings

Language Model

Text Classification

Seq2Seq Model

Conclusion

Improving the Model

Chapter Goals:

A. Model performance