Deep learning methods for natural language processing
Who is this presentation for?
- Data scientists, machine learning engineers, software engineers, and data engineers
- A basic understanding of Python, machine learning, and feed-forward neural networks
- Familiarity with TensorFlow (useful but not required)
Materials or downloads needed in advance
- A laptop with the most recent version of Docker installed
- Clone the course GitHub repository (Before the tutorial, follow the instructions in the course GitHub repository to set up your working environment. Note: It is not required that you follow along during the sessions. Approximately half of the session is focused on the models themselves and the underlying mathematics, with the other half involving going through implementations of the models in code. All of the models will be run in advance since they take a few hours to train.)
What you'll learn
- Learn how deep learning is applied to NLP-related tasks and how to implement these models in Python using TensorFlow
- Understand practical considerations for applying deep learning methods to business problems
Garrett Hoffman walks you through deep learning methods for natural language processing and natural language understanding tasks, using a live example in Python and TensorFlow with StockTwits data. Methods include word2vec, recurrent neural networks and variants (LSTM, GRU), and convolutional neural networks. You’ll explore use cases and motivations for using these methods, gain a conceptual intuition about how these models work, and briefly review the mathematics that underly each methodology.
Representation learning for text with word2vec word embeddings: The CBOW and skip gram models; how to train custom word embeddings; how to use pretrained word embeddings, such as those trained on Google News
Traditional recurrent neural networks (RNNs): Why these types of models often perform better than traditional alternatives; variants to traditional RNNs, such as long short-term memory (LSTM) cells and gated recurrent units (GRUs); why these models provide improvements in accuracy
Convolutional neural networks (CNNs): Why CNNs that are traditionally applied to computer vision are now being applied to language models; advantages that these have over RNNs; how RNNs can be used to learn generative models for text synthesis and the applications of this method
Garrett Hoffman is director of data science at StockTwits, where he leads efforts to use data science and machine learning to understand social dynamics and develop research and discovery tools that are used by a network of over one million investors. Garrett has a technical background in math and computer science but gets most excited about approaching data problems from a people-first perspective—using what we know or can learn about complex systems to drive optimal decisions, experiences, and outcomes.
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