Deep Learning: Build a Text Generator Model from Scratch

Explore the math behind tokenization and word-level embedding in transformers, building token IDs, embedding matrices, and the encoder–decoder flow to produce outputs.

Explore the math of positional encoding in transformers, combining word embeddings with sine and cosine terms using a 10,000 base to enable position-aware attention.

Learn how to implement multi-head self-attention from scratch, deriving query, key, and value matrices, computing attention scores, scaling by dk, applying softmax, and producing outputs with positional encoding.

Explore how a simple two-layer feed forward network processes attention outputs with weights, biases, and ReLU activation to produce a forward pass, illustrating the vanishing gradient concept.

Explain the math of layer normalization in a text generator: compute mean and variance, normalize with gamma and beta, then project back to vocabulary space.

Learn how a transformer uses word-level embeddings, self-attention, layer normalization, and a feed-forward network to project to the vocabulary space with a linear output layer and softmax for next-word prediction.

Set up the coding environment for a transformers from scratch project by installing Anaconda Navigator, launching a Jupyter notebook, and installing NumPy, Matplotlib, and Seaborn for visualization.

Build a transformer class with a constructor setting vocabulary size, dimension model, head size, encoder/decoder layers, dropout, and embedding and output projection with Xavier-style initialization.

Create the transformer forward pass by embedding inputs, applying positional encoding, running through encoder and decoder stacks, and producing next-token logits for training and inference.

Implement positional encoding for a text generator by building a sequence-length by model-dimension matrix using sine and cosine waves with a 10,000 divisor.

Visualize attention weights for a text generator by plotting self and cross attention maps using matplotlib and seaborn, mapping source tokens to target tokens and showing encoder and decoder dynamics.

Build a transformer-based text generator by implementing a generate function that encodes the input, applies positional encoding and a causal mask, and samples the next token with temperature and top-k.

Learn how softmax converts a vector to a probability distribution, enabling next-word selection in text generation, using numpy exponentials and max-subtraction to prevent overflow and normalize by sum of exponentials.

Implement dropout from scratch as a regularization technique during training by masking input vectors with a binomial-based dropout mask and scaling to preserve output expectations.

Implement the transformer training step, using source and target tokens with a learning rate of 0.001, computing logits, a forward pass, and cross-entropy loss to guide decoder token prediction.

Learn to build an encoder layer with multi-head self-attention, feedforward networks, and pre normalization, using residual connections and dropout to learn robust sequence representations for text generation.

Build a decoder layer in a transformer with masked self-attention, cross-attention to encoder outputs, and a two-layer feedforward network, reinforced by layer normalization, residual connections, and dropout for stability.

Implement multi-head self-attention from scratch, covering the forward pass, scaled dot-product attention, softmax, and the output projection with query, key, and value weights and Xavier initialization.

Implement a two-layer feedforward neural network for token-based, position-wise processing, with two linear layers, rectilinear activation, weights and bias initialization, and scaling to stabilize activations.

Implement layer normalization in a text generator by computing per-token mean and variance, applying epsilon for numerical stability, and learning gamma and beta to scale and shift the normalized output.

Learn to implement a transformer from scratch by stacking encoder and decoder layers, setting vocabulary mappings, configuring model dimensions, and running a full training and forward-pass loop.

Review the transformer implementation line by line, identify typos and syntax errors, and verify encoder, decoder, embedding, and attention components work together.

Explore token prediction analysis in a transformer-based text generator model from scratch, examine training loops and loss dynamics, and understand how data quality shapes next-word predictions.