Sequential Data Modeling with PyTorch | WSQ Courses

Originally published at: Sequential Data Modeling with PyTorch | WSQ Courses – WSQ courses | SkillsFuture Courses

Sequential data is the more prevalent data form such as text, speech, music, DNA sequence, video, drawing. Analysing sequential data is one of the key goals of machine learning such as document classification, time series forecasting, sentimental analysis, language translation.

We will show how to use Hugging Face Transformer for state of the art Natural Language Processing (NLP) applications. Hugging Face Transformers provides general-purpose architectures (BERT, GPT-2, RoBERTa, XLM, DistilBert, XLNet…) for Natural Language Understanding (NLU) and Natural Language Generation (NLG) with over 32+ pretrained models in 100+ languages and deep interoperability between Jax, PyTorch and TensorFlow.

Course Highlights

  • Recurrent Neural Network
  • Word Vectorization and Embedding
  • Text Classification
  • Transformers


All participants will receive a Certificate of Completion from Tertiary Courses after achieved at least 75% attendance.

Funding and Grant Applications

No funding is available for this course


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