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:
* Convert the encoder's final state into the proper format for decoding with attention
* Create a `BasicDecoder` object to use for decoding

# A. Creating the initial state

The final state from the encoder is a tuple containing an `LSTMStateTuple` object for each layer of the BiLSTM. However, if we want to use this as the initial state for an attention-wrapped decoder, we need to convert it into an <code><a href="https://www.tensorflow.org/api_docs/python/tf/contrib/seq2seq/AttentionWrapperState">AttentionWrapperState</a></code>.


For the conversion we ned to use `get_initial_state` with required arguments `inputs` , `batch_size` and `tf.float32` as `dtype`. The result is the initial state for the attention-wrapped decoder.

Below we demonstrate how to create the decoder's initial state from the encoder's final state.

Learn about the decoder object for the encoder-decoder model.

What you'll learn from this course

Word Embeddings

Language Model

Text Classification

Seq2Seq Model

Decoder Object

Chapter Goals:

A. Creating the initial state