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 softmax probabilities to make predictions for each time step</li>
</ul>


<h2>A. Calculating probabilities</h2>

<div id="section1_l8" class="collapse show">
<p>When using a feed-forward neural network for multiclass predictions, we first need to convert the model's logits into probabilities with the softmax function. For an RNN language model, the idea is the exact same. After converting the RNN's outputs into logits, we apply the softmax function to the <i>final dimension</i> of the logits.</p>

The model we've built so far in this section predicts the likely next word given an input sentence (see the __Language Model__ chapter for a refresher)


<h2>Chapter Goals:</h2>
<ul>
	<li>Calculate softmax probabilities to make predictions for each time step</li>
</ul>


<h2>A. Calculating probabilities</h2>

<div id="section1_l8">
<p>When using a feed-forward neural network for multiclass predictions, we first need to convert the model's logits into probabilities with the softmax function. For an RNN language model, the idea is the exact same. After converting the RNN's outputs into logits, we apply the softmax function to the <i>final dimension</i> of the logits.</p>

The model we've built so far in this section predicts the likely next word given an input sentence (see the __Language Model__ chapter for a refresher)


Create word predictions based on the output of your LSTM model.

What you'll learn from this course

Word Embeddings

Language Model

Text Classification

Seq2Seq Model

Predictions

Chapter Goals:

A. Calculating probabilities