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 the encoder-decoder model architecture</li>
</ul>


<h2>A. Model architecture</h2>

<div id="section1_l4" class="collapse show">
<p>As previously mentioned, the encoder portion of the encoder-decoder model in this section is a BiLSTM. The decoder portion is just a regular forward LSTM, with the same number of LSTM layers as the encoder.</p>
<p>What makes an encoder-decoder model so powerful is that the decoder uses the final state of the encoder as its initial state. This gives the decoder access to the information that the encoder extracted from the input sequence, which is crucial for good sequence to sequence modeling.</p>
<p>In the case of multiple layers for the encoder-decoder model, each layer's final state in the encoder acts as the initial state for the corresponding layer in the decoder.</p>
</div>


<h2>Chapter Goals:</h2>
<ul>
	<li>Learn about the encoder-decoder model architecture</li>
</ul>


<h2>A. Model architecture</h2>

<div id="section1_l4">
<p>As previously mentioned, the encoder portion of the encoder-decoder model in this section is a BiLSTM. The decoder portion is just a regular forward LSTM, with the same number of LSTM layers as the encoder.</p>
<p>What makes an encoder-decoder model so powerful is that the decoder uses the final state of the encoder as its initial state. This gives the decoder access to the information that the encoder extracted from the input sequence, which is crucial for good sequence to sequence modeling.</p>
<p>In the case of multiple layers for the encoder-decoder model, each layer's final state in the encoder acts as the initial state for the corresponding layer in the decoder.</p>
</div>


Learn about the encoder-decoder model architecture.

What you'll learn from this course

Word Embeddings

Language Model

Text Classification

Seq2Seq Model

Encoder-Decoder

Chapter Goals:

A. Model architecture