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

# Chapter Goals:
<ul>
	<li>Learn strategies for improving an encoder-decoder model</li>
	<li>Run the encoder-decoder model in inference mode</li>
</ul>


# A. Training strategies

<div id="section1_l11" class="collapse show">
<p>Good encoder-decoder models tend to have a large number of weight parameters, since they consist of large LSTM/BiLSTM layers. Because of this, it usually takes a long time to train an encoder-decoder model to convergence.</p>
<p>To speed up training, it might be beneficial to use a larger batch size during the initial stages of training, then reduce the batch size once the model begins to show considerable improvement in reducing the loss function. A larger batch size allows the model to get through more data, and as long as we reduce the batch size at an early enough stage the model won't miss its convergence point.</p>
<p>Another strategy to speed up training is to start off with a larger learning rate, then gradually decrease the learning rate as the model trains.</p>
</div>


# A. Training strategies

<div id="section1_l11">
<p>Good encoder-decoder models tend to have a large number of weight parameters, since they consist of large LSTM/BiLSTM layers. Because of this, it usually takes a long time to train an encoder-decoder model to convergence.</p>
<p>To speed up training, it might be beneficial to use a larger batch size during the initial stages of training, then reduce the batch size once the model begins to show considerable improvement in reducing the loss function. A larger batch size allows the model to get through more data, and as long as we reduce the batch size at an early enough stage the model won't miss its convergence point.</p>
<p>Another strategy to speed up training is to start off with a larger learning rate, then gradually decrease the learning rate as the model trains.</p>
</div>


Learn strategies for improving an encoder-decoder model.

What you'll learn from this course

Word Embeddings

Language Model

Text Classification

Seq2Seq Model

Model Improvement

Chapter Goals:

A. Training strategies