Sequence Modeling

This Sequence Modeling training course will teach you how to build models for natural language, audio, and other sequence data. Thanks to deep learning, sequence algorithms are working far better than just two years ago, and this is enabling numerous exciting applications in speech recognition, music synthesis, chatbots, machine translation, natural language understanding, and many others.

This is also partnering with the NVIDIA Deep Learning Institute (DLI) in Course 5, Sequence Models, to provide a programming assignment on Machine Translation with deep learning.

Any professional from domains (e.g. healthcare, finance, manufacturing) that need to manage and work with data.

Data engineers, researchers, healthcare professionals and more.

By attending Sequence Modeling workshop, Participants will: 

  • Understand how to build and train Recurrent Neural Networks (RNNs), and commonly-used variants such as GRUs and LSTMs.
  • Be able to apply sequence models to natural language problems, including text synthesis. 
  • Be able to apply sequence models to audio applications, including speech recognition and music synthesis.

COURSE AGENDA

  • Basic Models
  • Picking the most likely sentence
  • Beam Search
  • Refinements to Beam Search
  • Error analysis in beam search
  • Bleu Score (optional)
  • Attention Model Intuition
  • Attention Model
  • Speech recognition
  • Trigger Word Detection
  • Word Representation
  • Using word embeddings
  • Properties of word embeddings
  • Embedding matrix
  • Learning word embeddings
  • Word2Vec
  • Negative Sampling
  • GloVe word vectors
  • Sentiment Classification
  • Debiasing word embeddings
  • Why sequence models
  • Notation
  • Recurrent Neural Network Model
  • Backpropagation through time
  • Different types of RNNs
  • Language model and sequence generation
  • Sampling novel sequences
  • Vanishing gradients with RNNs
  • Gated Recurrent Unit (GRU)
  • Long Short Term Memory (LSTM)
  • Bidirectional RNN
  • Deep RNNs

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