Transfrmers - Roadmap

This roadmap is structured to help you develop deep, interview-ready mastery of Transformer architectures — from theory and math intuition to scalable engineering practices.
Each topic emphasizes why it matters, how to learn it deeply, and what probing questions interviewers use to test your real understanding.

⚙️ Core ML & Deep Learning Foundations

1.1: Fundamentals of Representation Learning

1. Revisit why deep learning shifted from feature engineering to learned representations.
2. Understand embeddings as dense, learned encodings of input spaces.
3. Visualize embeddings (e.g., t-SNE on word vectors) to see how meaning clusters.

Deeper Insight: Interviewers might ask: “What’s the difference between one-hot encoding and embeddings?” or “Why are embeddings lower-dimensional?”
Discuss the curse of dimensionality and how embeddings act as a continuous latent space that preserves semantic proximity.

1.2: Sequence Modeling Evolution

1. Start with RNNs and LSTMs — understand recurrence, hidden states, and vanishing gradients.
2. Identify why RNNs fail for long dependencies (gradient decay, serial computation).
3. Transition to self-attention as a parallelizable, global dependency mechanism.

Deeper Insight: You may be asked to “compare LSTMs vs. Transformers in terms of compute, memory, and sequence length scaling.”
Be ready to derive O(n) vs. O(n²) complexity and explain why attention scales worse but trains faster.

1.3: Gradient-Based Optimization and Training Dynamics

1. Review backpropagation and the chain rule in matrix form.
2. Understand the vanishing/exploding gradient problem in deep networks.
3. Learn optimization tricks used in Transformers: AdamW, LayerNorm, gradient clipping.

Probing Question: “Why is AdamW preferred over Adam?”
Explain how decoupled weight decay stabilizes training by preventing weight magnitude explosion, a critical issue in large language models.

🧩 Transformer Architecture — From Scratch to Scale

2.1: The Self-Attention Mechanism

1. Derive the Scaled Dot-Product Attention:
   \[ \text{Attention}(Q, K, V) = \text{softmax}\left( \frac{QK^T}{\sqrt{d_k}} \right)V \]
2. Implement this in NumPy or PyTorch to solidify understanding.
3. Visualize attention maps — see how tokens attend differently across layers.

Deeper Insight: Be ready to explain why scaling by √dₖ stabilizes gradients.
A common probing question: “What happens if you remove the scaling factor?” → gradients explode as dot products grow large.

2.2: Multi-Head Attention

1. Understand that multiple attention heads allow diverse subspace projections.
2. Compute parameter counts and memory implications per head.
3. Code a multi-head attention module manually to understand linear projections and concatenation.

Probing Question: “Why not just one wide head?”
Answer: Multiple heads allow the model to capture heterogeneous relationships — syntactic vs. semantic — at different representational subspaces.

2.3: Positional Encoding

1. Explore why Transformers need positional information due to permutation invariance.
2. Derive sinusoidal encodings and understand periodicity.
3. Implement learned positional embeddings and compare performance empirically.

Deeper Insight: Expect to be asked: “What happens if you remove positional encoding?”
The model loses order-awareness; outputs become bag-of-words-like.
Discuss alternatives like relative positional encoding and rotary embeddings (RoPE).

2.4: Feed-Forward Layers and Residual Connections

1. Understand the “FFN sandwich”: Attention → Add & Norm → FFN → Add & Norm.
2. Study why residuals accelerate training and stabilize gradients.
3. Explore LayerNorm’s role in ensuring consistent activation scales.

Probing Question: “Why is LayerNorm placed before or after attention in different variants?”
Compare Pre-Norm (Transformer-XL, GPT) vs. Post-Norm (original Transformer) architectures.

2.5: Encoder vs. Decoder Architecture

1. Compare encoder-only (BERT) vs. decoder-only (GPT) vs. encoder-decoder (T5) paradigms.
2. Identify which use bidirectional vs. causal masking.
3. Implement causal masking in code — understand why it prevents “future leakage.”

Deeper Insight: In interviews, expect to reason about where attention is masked and how it affects context flow across architectures.

🧮 Mathematics Behind Transformers

3.1: Linear Algebra Refresher

1. Master matrix multiplication and projection geometry — crucial for Q, K, V transformations.
2. Understand orthogonality, covariance, and subspace decomposition.
3. Study the eigenspectrum of weight matrices and its relation to training stability.

Probing Question: “Why do we project Q, K, and V into separate subspaces?”
To allow asymmetric relational modeling — a query’s semantics differ from the key’s retrieval role.

3.2: Softmax and Normalization Effects

1. Analyze the exponential nature of Softmax — derive its gradient.
2. Study how temperature scaling affects attention sharpness.
3. Understand how Softmax normalization distributes focus across tokens.

Deeper Insight: “What happens if temperature → 0 or ∞?”
Low temperature → deterministic attention; high temperature → uniform attention. Be ready to discuss trade-offs.

3.3: Initialization and Training Stability

1. Study Xavier and Kaiming initialization for maintaining gradient variance.
2. Explore how LayerNorm interacts with initialization to prevent exploding activations.
3. Experiment with removing normalization to observe training collapse.

Probing Question: “Why is initialization critical in large Transformers?”
Because deeper layers amplify small imbalances, leading to gradient vanishing or instability during long training runs.

🧱 Engineering & Scaling Transformers

4.1: Efficient Attention Mechanisms

1. Study the memory complexity of standard attention: O(n²).
2. Learn approximations: Linformer, Performer, FlashAttention, Longformer.
3. Compare trade-offs between exactness, latency, and memory footprint.

Probing Question: “How would you design an attention mechanism for 100k tokens?”
Discuss sparse attention, chunking, and kernelized approximations.

4.2: Model Parallelism and Sharding

1. Understand tensor, pipeline, and data parallelism.
2. Study how gradient synchronization works in distributed setups.
3. Learn libraries: DeepSpeed, Megatron-LM, FSDP.

Deeper Insight: “How do you avoid communication bottlenecks?”
Discuss gradient accumulation, ZeRO optimizations, and mixed-precision training.

4.3: Fine-Tuning and Transfer Learning

1. Learn pretraining vs. fine-tuning strategies.
2. Explore parameter-efficient fine-tuning (LoRA, adapters, prefix-tuning).
3. Implement LoRA manually to understand low-rank weight updates.

Probing Question: “Why use LoRA instead of full fine-tuning?”
Reduced compute, fewer parameters to update, faster convergence on limited data.

4.4: Evaluation & Interpretability

1. Understand perplexity and loss metrics for language models.
2. Visualize attention weights to interpret model focus.
3. Explore probing tasks for syntactic and semantic knowledge.

Deeper Insight: Expect “How do you verify your model isn’t overfitting memorized patterns?”
Discuss data splitting, attention probing, and interpretability audits.

🧠 Advanced Topics & Research Extensions

5.1: Transformer Variants

1. Study Vision Transformers (ViT), Reformer, Perceiver, Sparse Transformer.
2. Compare architectural innovations: locality, recurrence, compression.
3. Map how these trade off between accuracy and compute.

Deeper Insight: Be prepared to discuss why ViT works despite limited inductive bias — and when convolution still wins.

5.2: Scaling Laws and Model Efficiency

1. Learn empirical scaling laws (Kaplan et al., 2020): loss ∝ power-law(size).
2. Discuss compute-optimal training and data–model trade-offs.
3. Analyze diminishing returns and parameter redundancy.

Probing Question: “If you double model size but not data, what happens?”
You’ll overfit faster — data and compute must scale jointly.

5.3: Prompting, In-Context Learning, and RLHF

1. Understand prompting mechanics and emergent few-shot abilities.
2. Study instruction tuning and reward models in RLHF.
3. Grasp how alignment affects generalization and bias mitigation.

Deeper Insight: “Why does in-context learning emerge?”
Because Transformers implicitly perform meta-learning during training — they learn to infer tasks from prompt patterns.