Gain insights into processing text data, creating word embeddings, and using LSTMs for semantic analysis and machine translation. Explore industry-relevant NLP techniques with Python and TensorFlow.

nlp_docker.tar.gz

mnist-fashion

In this course you'll learn techniques for processing text data, creating word embeddings, and using long short-term memory networks (LSTM) for tasks such as semantic analysis and machine translation. After completing this course, you will be able to solve the important day-to-day NLP problems faced in industry, which is incredibly useful given the prevalence of text data.

The code for this course is built around the TensorFlow framework, one of the premier frameworks for industry machine learning, and the Python pandas library for data analysis. Knowledge of Python and TensorFlow are prerequisites. 

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

Natural Language Processing with Machine Learning

<h2 class="section-title">Chapter Goals:</h2>
<ul>
	<li>Learn about sequence lengths and padding</li>
</ul>


<h2>A. Varied length sequence</h2>

<div id="section1_l2" class="collapse show">
<p>When dealing with most neural networks, the input data always has a fixed length. This is because most neural networks have what's known as a <i>feed-forward</i> structure, meaning that they utilize multiple layers of fixed sizes to compute the network's output.</p>
<p>However, since text data involves different sized text sequences (e.g. sentences, passages, etc.), our language model needs to be able to handle input data of varied lengths. Therefore, we use a <i>recurrent</i> neural network (discussed in greater detail in upcoming chapters) for the basis of our language model.</p>
</div>


<h2>Chapter Goals:</h2>
<ul>
	<li>Learn about sequence lengths and padding</li>
</ul>


<h2>A. Varied length sequence</h2>

<div id="section1_l2">
<p>When dealing with most neural networks, the input data always has a fixed length. This is because most neural networks have what's known as a <i>feed-forward</i> structure, meaning that they utilize multiple layers of fixed sizes to compute the network's output.</p>
<p>However, since text data involves different sized text sequences (e.g. sentences, passages, etc.), our language model needs to be able to handle input data of varied lengths. Therefore, we use a <i>recurrent</i> neural network (discussed in greater detail in upcoming chapters) for the basis of our language model.</p>
</div>


Understand the purpose of padding with respect to tokenized sequences.

What you'll learn from this course

Word Embeddings

Language Model

Text Classification

Seq2Seq Model

Padding

Chapter Goals:

A. Varied length sequence