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 final states for an LSTM and BiLSTM</li>
</ul>


<h2>A. The encoder</h2>

<div id="section1_l2" class="collapse show">
<p>In an encoder-decoder model for seq2seq tasks, there are two components: the encoder and the decoder. The encoder is responsible for extracting useful information from the input sequence. For NLP applications, the encoder is normally an LSTM or BiLSTM.</p>
</div>


<h2>B. LSTM final state</h2>

<div id="section2_l2" class="collapse show">
<p>When using an LSTM or BiLSTM encoder, we need to pass the final state of the encoder into the decoder. The final state of an LSTM in TensorFlow is represented by the <code><a href="https://www.tensorflow.org/api_docs/python/tf/nn/rnn_cell/LSTMStateTuple" target="_blank">LSTMStateTuple</a></code> object.</p>



<h2>Chapter Goals:</h2>
<ul>
	<li>Learn about the final states for an LSTM and BiLSTM</li>
</ul>


<h2>A. The encoder</h2>

<div id="section1_l2">
<p>In an encoder-decoder model for seq2seq tasks, there are two components: the encoder and the decoder. The encoder is responsible for extracting useful information from the input sequence. For NLP applications, the encoder is normally an LSTM or BiLSTM.</p>
</div>


<h2>B. LSTM final state</h2>

<div id="section2_l2">
<p>When using an LSTM or BiLSTM encoder, we need to pass the final state of the encoder into the decoder. The final state of an LSTM in TensorFlow is represented by the <code><a href="https://www.tensorflow.org/api_docs/python/tf/nn/rnn_cell/LSTMStateTuple" target="_blank">LSTMStateTuple</a></code> object.</p>



Learn about the final state output of an LSTM and BiLSTM.

What you'll learn from this course

Word Embeddings

Language Model

Text Classification

Seq2Seq Model

Final States

Chapter Goals:

A. The encoder

B. LSTM final state