Intermediate Concepts in AI Research
Once you’ve mastered the fundamentals, the next step is understanding how today’s large-scale systems actually work. These twenty-four intermediate concepts form the bridge between textbook theory and production-grade AI research.
Self-Attention
The mechanism that allows a model to focus on different parts of its input sequence when computing representations, central to Transformers.
Positional Encoding
Extra information added to token embeddings to give models a sense of sequence order.
Multi-Head Attention
Running several attention operations in parallel so the model can attend to information from multiple representational subspaces.
Residual Connection
A shortcut that adds input activations directly to outputs of a layer, improving gradient flow and stability in deep networks.
Layer Normalization
Normalizes neuron activations within a layer to stabilize training and prevent internal covariate shift.
Feedforward Network (FFN)
The two-layer MLP inside Transformer blocks that expands and projects representations after attention.
Tokenizer
The component that converts text into discrete numerical tokens for model input; common schemes include Byte Pair Encoding (BPE) and SentencePiece.
Vocabulary
The set of unique tokens known to a language model; determines what units of meaning it can directly represent.
Context Window
The maximum number of tokens a model can process at once; limits the length of input prompts and output generations.
Pretraining
The large-scale, general-purpose learning phase where models absorb broad patterns from massive datasets before task-specific fine-tuning.
Transfer Learning
Leveraging knowledge from one model or task to accelerate learning on another related task.
Adapter Layers
Small trainable modules inserted into a frozen model to enable parameter-efficient fine-tuning.
Low-Rank Adaptation (LoRA)
A specific adapter method that decomposes updates into low-rank matrices, greatly reducing training cost and memory use.
Quantization-Aware Training (QAT)
Training models while simulating reduced precision arithmetic so they perform well even after quantization.
Gradient Checkpointing
A memory-saving technique that recomputes certain activations during backward pass instead of storing them.
Mixed Precision Training
Using both 16-bit and 32-bit floating-point operations to accelerate training while preserving numerical stability.
Distributed Data Parallel (DDP)
A framework that splits batches across multiple GPUs or nodes, synchronizing gradients efficiently during training.
Model Parallelism
Splitting different parts of a model across multiple devices when it’s too large to fit into one GPU’s memory.
Pipeline Parallelism
Dividing model layers into stages that run concurrently across hardware, improving throughput for huge networks.
Optimizer
The algorithm that updates weights during training (e.g., Adam, Adafactor, SGD). Choice affects convergence and generalization.
Learning Rate Scheduler
A policy that changes the learning rate during training (warmup, cosine decay, etc.) to improve stability.
Regularization Losses
Extra terms (like L1/L2 or KL divergence) added to the main loss to impose smoothness, sparsity, or diversity constraints.
Checkpoint
A saved snapshot of a model’s parameters and optimizer state that allows training to resume or evaluation to occur later.
Evaluation Dataset
A held-out portion of data used only for measuring performance and detecting overfitting, not for learning.