Congratulations!

1. Congratulations!

Congratulations! You came a long way to the universe of language models using python and keras.

2. Wrap-up

In this course, you learned four different applications of language models using keras. sentiment classification multiclass classification text generation and neural machine translation Also, you learned the different types of sequence to sequence models Finally, you learned the main modules and packages in keras that make it possible to implement the models.

3. RNN pitfalls and different cell types

You also learned about some pitfalls that occurs when implementing RNN models: the vanishing and exploding gradient problems. To deal with those problems, you can use the GRU or LSTM cells. Also, you learned how to implement the Embedding layer to create and/or use word vectors. Finally, you applied a few tuning techniques to improve the performance of the sentiment analysis model.

4. Multi-class classification

Later on, you learned how to perform multi-class classification. You learned how to prepare the data how to use trained word vectors for transfer learning how to create the models in Keras and how to access the models' performance

5. Text generation and NMT

In text generation, you used characters as tokens and learned the necessary steps to transform the text into sequence of characters and next character arrays. Also, you created a function to generate sentences mimicking Sheldon from The Big Bang Theory In neural machine translation, you used words as tokens Learned how to prepare the different languages text data as encoder and decoder. Then you learned how to build a NMT model in Keras and used it to translate Portuguese small sentences into English.

6. Congratulations!!!

Again, congratulations for finishing this course. I hope that you can use what you learned to other similar problems of your interest.

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This exercise is part of the course

Recurrent Neural Networks (RNNs) for Language Modeling with Keras

AdvancedSkill Level

4.8+

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In this chapter, you will learn the foundations of Recurrent Neural Networks (RNN). Starting with some prerequisites, continuing to understanding how information flows through the network and finally seeing how to implement such models with Keras in the sentiment classification task.

Exercise 1: Introduction to the course Exercise 2: Comparing the number of parameter of RNN and ANN Exercise 3: Sentiment analysis Exercise 4: Sequence to sequence models Exercise 5: Introduction to language models Exercise 6: Getting used to text data Exercise 7: Preparing text data for model input Exercise 8: Transforming new text Exercise 9: Introduction to RNN inside Keras Exercise 10: Keras models Exercise 11: Keras preprocessing Exercise 12: Your first RNN model

You will learn about the vanishing and exploding gradient problems, often occurring in RNNs, and how to deal with them with the GRU and LSTM cells. Furthermore, you'll create embedding layers for language models and revisit the sentiment classification task.

Exercise 1: Vanishing and exploding gradients Exercise 2: Exploding gradient problem Exercise 3: Vanishing gradient problem Exercise 4: GRU and LSTM cells Exercise 5: GRU cells are better than simpleRNN Exercise 6: Stacking RNN layers Exercise 7: The Embedding layer Exercise 8: Number of parameters comparison Exercise 9: Transfer learning Exercise 10: Embeddings improves performance Exercise 11: Sentiment classification revisited Exercise 12: Better sentiment classification Exercise 13: Using the CNN layer

Next, in this chapter you will learn how to prepare data for the multi-class classification task, as well as the differences between multi-class classification and binary classification (sentiment analysis). Finally, you will learn how to create models and measure their performance with Keras.

Exercise 1: Data pre-processing Exercise 2: Prepare label vectors Exercise 3: Pre-process data Exercise 4: Transfer learning for language models Exercise 5: Transfer learning starting point Exercise 6: Word2Vec Exercise 7: Multi-class classification models Exercise 8: Exploring 20 News Groups dataset Exercise 9: Classifying news articles Exercise 10: Assessing the model's performance Exercise 11: Precision-Recall trade-off Exercise 12: Precision or Recall, that is the question Exercise 13: Performance on multi-class classification

This chapter introduces you to two applications of RNN models: Text Generation and Neural Machine Translation. You will learn how to prepare the text data to the format needed by the models. The Text Generation model is used for replicating a character's way of speech and will have some fun mimicking Sheldon from The Big Bang Theory. Neural Machine Translation is used for example by Google Translate in a much more complex model. In this chapter, you will create a model that translates Portuguese small phrases into English.

Exercise 1: Sequence to Sequence Models Exercise 2: Text generation examples Exercise 3: NMT example Exercise 4: The Text Generating Function Exercise 5: Predict next character Exercise 6: Generate sentence with context Exercise 7: Change the probability scale Exercise 8: Text Generation Models Exercise 9: Create vectors of sentences and next characters Exercise 10: Preparing the data for training Exercise 11: Creating the text generation model Exercise 12: Neural Machine Translation Exercise 13: Preparing the input text Exercise 14: Preparing the output text Exercise 15: Translate Portuguese to English Exercise 16: Congratulations!

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