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>Calculate the model's loss using sigmoid cross entropy</li>
</ul>


<h2>A. Sigmoid cross entropy</h2>

<div id="section1_l5" class="collapse show">
<p>The task for our model is to classify input text sequences as either negative (label <code>0</code>) or positive (label <code>1</code>). This is equivalent to binary classification. As with regular binary classification, we use sigmoid cross entropy to calculate the model's loss.</p>
<p>For an in-depth and intuitive explanation of sigmoid cross entropy and binary classification, check out the <a href="https://www.educative.io/collection/6083138522447872/5629499534213120?authorName=Adaptilab" target="_blank">Machine Learning for Software Engineers</a> course on Educative.
</div>


<h2>Chapter Goals:</h2>
<ul>
	<li>Calculate the model's loss using sigmoid cross entropy</li>
</ul>


<h2>A. Sigmoid cross entropy</h2>

<div id="section1_l5">
<p>The task for our model is to classify input text sequences as either negative (label <code>0</code>) or positive (label <code>1</code>). This is equivalent to binary classification. As with regular binary classification, we use sigmoid cross entropy to calculate the model's loss.</p>
<p>For an in-depth and intuitive explanation of sigmoid cross entropy and binary classification, check out the <a href="https://www.educative.io/collection/6083138522447872/5629499534213120?authorName=Adaptilab" target="_blank">Machine Learning for Software Engineers</a> course on Educative.
</div>


Calculate the model's sigmoid cross entropy loss.

What you'll learn from this course

Word Embeddings

Language Model

Text Classification

Seq2Seq Model

Loss

Chapter Goals:

A. Sigmoid cross entropy