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Course 3 : Advanced Networks for Language models

About Course

  1. Attention Mechanisms: Attention mechanisms, often integrated with RNNs or Transformers, allow models to focus on specific parts of the input sequence when making predictions. This has significantly improved performance in tasks involving long sequences or where certain parts of the input are more relevant than others.
  2. Transformer Models: Transformers have gained immense popularity, especially with models like the Transformer architecture used in the Transformer model and its variants (e.g., BERT, GPT). Transformers rely entirely on self-attention mechanisms and eschew recurrence, offering parallel computation and capturing dependencies across the sequence more effectively.
  3. BERT (Bidirectional Encoder Representations from Transformers): BERT introduced the concept of bidirectional context understanding in pre-training language representations. By pre-training on large corpora with masked language modeling objectives, BERT has achieved state-of-the-art results in various natural language understanding tasks.
  4. GPT (Generative Pre-trained Transformer): GPT models, such as GPT-2 and GPT-3, leverage transformer architectures for generating coherent and contextually relevant text. These models have demonstrated remarkable capabilities in tasks like text completion, summarization, and text generation.
  5. BERT-based Sequence Classification Models: Models like RoBERTa, ALBERT, and ELECTRA are based on BERT but incorporate enhancements to improve performance, reduce parameters, or speed up training.
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Course Content

Transformer architecture

  • Background on transformer models
  • Architecture – High level view

Understanding the ENCODER block

Transformer Blocks

Training and Optimization of Transformer Models

Applications and Variants of Transformer Models

understanding the BERT model

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