Understanding the Transformer Architecture

The Encoder-Decoder Structure

• Learn the fundamental architecture of the Transformer model, including the encoder and decoder stacks.
• Understand the role of the encoder in processing the input sequence into contextualized representations.
• Learn how the decoder uses these representations to generate the output sequence, step by step.
• Explore the concept of residual connections and layer normalization, crucial for training deep networks.

Attention Mechanisms: Self-Attention and Source-Target Attention

• Master the concept of self-attention, which allows the model to weigh the importance of different parts of the input sequence when processing each word.
• Understand how self-attention captures long-range dependencies within the input sequence, addressing the limitations of recurrent neural networks.
• Delve into the mathematics of self-attention, including the calculation of attention weights using queries, keys, and values.
• Learn about multi-headed attention, which allows the model to capture different types of relationships within the data by using multiple sets of attention weights.
• Explore source-target attention (also known as encoder-decoder attention), which enables the decoder to attend to the relevant parts of the encoder output.
• Understand how source-target attention allows the decoder to focus on the most important information from the input sequence when generating the output.

Positional Encoding

• Learn why positional encoding is necessary to provide the Transformer model with information about the order of words in the input sequence.
• Understand different methods of positional encoding, including sinusoidal positional encodings and learned positional embeddings.
• Implement and compare different positional encoding schemes.

Implementing the Transformer Model

Building the Encoder and Decoder Layers

• Implement the encoder layer, which consists of a multi-headed self-attention sublayer followed by a feed-forward network.
• Implement the decoder layer, which includes a multi-headed self-attention sublayer, a source-target attention sublayer, and a feed-forward network.
• Understand the importance of residual connections and layer normalization in each layer.

Masking Techniques

• Learn about padding masks, which prevent the model from attending to padding tokens in the input sequence.
• Understand future masking (also known as causal masking), which prevents the decoder from attending to future tokens in the output sequence during training.
• Implement both padding and future masking in your Transformer model.

The Feed-Forward Network

• Understand the role of the feed-forward network in transforming the output of the attention sublayers.
• Implement the feed-forward network using linear layers and non-linear activation functions.

Training and Optimization

Data Preparation

• Understand the importance of tokenization and vocabulary creation for neural machine translation.
• Learn how to create a vocabulary from a corpus of parallel text.
• Implement data batching and padding for efficient training.

Loss Functions and Optimization Algorithms

• Understand the use of cross-entropy loss for training neural machine translation models.
• Implement label smoothing to improve the generalization performance of the model.
• Explore different optimization algorithms, such as Adam and Adafactor, and their impact on training.
• Learn about learning rate scheduling techniques, such as the inverse square root schedule, which are commonly used in Transformer training.

Regularization Techniques

• Understand the importance of regularization techniques, such as dropout and weight decay, for preventing overfitting.
• Implement dropout in the attention sublayers and feed-forward networks.

Advanced Techniques and Architectures

Scaling Transformers

• Understand the challenges of training very large Transformer models.
• Learn about techniques for scaling Transformers, such as model parallelism and data parallelism.
• Explore gradient accumulation to train with large batch sizes on limited hardware.

Transformer Variants

• Explore different Transformer variants, such as BERT, GPT, and BART.
• Understand the key differences between these variants and their applications in different NLP tasks.

Attention Visualization and Interpretation

• Learn how to visualize attention weights to understand what the model is attending to during translation.
• Interpret attention patterns to gain insights into the model's behavior and identify potential areas for improvement.

Practical Applications

Machine Translation Deployment

• Learn how to deploy a trained Transformer model for real-time machine translation.
• Understand the challenges of deploying large models and techniques for optimizing inference speed.

Beyond Machine Translation

• Explore the applications of the Transformer architecture in other NLP tasks, such as text summarization, question answering, and text generation.