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 bidirectional LSTM and why it's used</li>
</ul>


<h2>A. Forwards and backwards</h2>

The language model from the <b>Language Model</b> section of this course used a regular LSTM which read each input sequence in the <i>forwards</i> direction. This meant that the recurrent connections went in the left-right direction, i.e. from time step $t$ to time step $t + 1$.
<p>While regular LSTMs work well for most NLP tasks, they are not always the best option. Specifically, when we have access to a completed text sequence (e.g. text classification), it may be beneficial to look at the sequence in both the forwards and <i>backwards</i> directions.</p>
<p>By looking at a sequence in both directions, the model can take into account both the past and future context of words and phrases in the text sequence, which allows it to obtain a better understanding of the text sequence.</p>


<h2>Chapter Goals:</h2>
<ul>
	<li>Learn about the bidirectional LSTM and why it's used</li>
</ul>


Create and run a bidirectional LSTM model.

What you'll learn from this course

Word Embeddings

Language Model

Text Classification

Seq2Seq Model

Bidirectional LSTM

Chapter Goals:

A. Forwards and backwards