A handful of example natural language processing (NLP) and natural language understanding (NLU) problems. These are also often referred to as sequence problems (going from one sequence to another).

The main goal of natural language processing (NLP) is to derive information from natural language.

Natural language is a broad term but you can consider it to cover any of the following:

• Text (such as that contained in an email, blog post, book, Tweet)
• Speech (a conversation you have with a doctor, voice commands you give to a smart speaker)

Under the umbrellas of text and speech there are many different things you might want to do.

If you're building an email application, you might want to scan incoming emails to see if they're spam or not spam (classification).

If you're trying to analyse customer feedback complaints, you might want to discover which section of your business they're for.

To get hands-on with NLP in TensorFlow, we're going to practice the steps we've used previously but this time with text data:

Text -> turn into numbers -> build a model -> train the model to find patterns -> use patterns (make predictions)

Let's get specific hey?

• Downloading a text dataset
• Visualizing text data
• Converting text into numbers using tokenization
• Turning our tokenized text into an embedding
• Modelling a text dataset
  • Starting with a baseline (TF-IDF)
  • Building several deep learning text models
    • Dense, LSTM, GRU, Conv1D, Transfer learning
• Comparing the performance of each our models
• Combining our models into an ensemble
• Saving and loading a trained model
• Find the most wrong predictions