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 training loss based on the model's logits and final token sequences</li>
</ul>


<h2>A. Final token sequence</h2>

<div id="section1_l9" class="collapse show">
<p>So far, we've used the input sequences and ground truth sequences for training the encoder-decoder model. The final token sequences are used when calculating the loss.</p>
<p>If we view the decoder as a language model, the ground truth sequences act as the language model's input while the final token sequences act as the "correct" output for the language model.</p>
<p>In a language model, we calculate the loss based on the model's logits and the "correct" output. For the encoder-decoder, the loss is calculated in the same way. We use a sparse softmax cross entropy loss based on the logits and final token sequences, and then zero-out the time steps that correspond to padding.</p>
</div>


<h2>Chapter Goals:</h2>
<ul>
	<li>Calculate the training loss based on the model's logits and final token sequences</li>
</ul>


<h2>A. Final token sequence</h2>

<div id="section1_l9">
<p>So far, we've used the input sequences and ground truth sequences for training the encoder-decoder model. The final token sequences are used when calculating the loss.</p>
<p>If we view the decoder as a language model, the ground truth sequences act as the language model's input while the final token sequences act as the "correct" output for the language model.</p>
<p>In a language model, we calculate the loss based on the model's logits and the "correct" output. For the encoder-decoder, the loss is calculated in the same way. We use a sparse softmax cross entropy loss based on the logits and final token sequences, and then zero-out the time steps that correspond to padding.</p>
</div>


Calculate the model's loss based on logits and sparse outputs.

What you'll learn from this course

Word Embeddings

Language Model

Text Classification

Seq2Seq Model

Calculating Loss

Chapter Goals:

A. Final token sequence