You have learned how to work with text classification with Keras, and we have gone from a bag-of-words model with logistic regression to increasingly more advanced methods leading to convolutional neural networks.
You should be now familiar with word embeddings, why they are useful, and also how to use pretrained word embeddings for your training. You have also learned how to work with neural networks and how to use hyperparameter optimization to squeeze more performance out of your model.
One big topic which we have not covered here left for another time was recurrent neural networks, more specifically LSTM and GRU. Those are other powerful and popular tools to work with sequential data like text or time series. Other interesting developments are currently in neural networks that employ attention which are under active research and seem to be a promising next step since LSTM tend to be heavy on the computation.
You can use this knowledge and the models that you have trained on an advanced project as in this tutorial to employ sentiment analysis on a continuous stream of twitter data with Kibana and Elasticsearch. You could also combine sentiment analysis or text classification with speech recognition like in this handy tutorial using the SpeechRecognition library in Python.
If you want to delve deeper into the various topics from this article you can take a look at these links:
- AI researchers allege that machine learning is alchemy
- When Will AI Exceed Human Performance? Evidence from AI Experts
- Keras Code Examples
- Deep Learning, NLP, and Representations
- Word2Vec Paper
- GloVe Paper
Congratulations, you made it to the end of the course! What’s your #1 takeaway or favorite thing you learned? How are you going to put your newfound skills to use? Leave a comment in the discussion section and let us know.