Results for "deep learning"
Deep Learning
IntermediateA branch of ML using multi-layer neural networks to learn hierarchical representations, often excelling in vision, speech, and language.
Deep Learning is a type of machine learning that uses structures called neural networks, which are inspired by how the human brain works. Imagine a series of layers where each layer learns to recognize different features of an image, like edges, shapes, and eventually, whole objects. This is how ...
A datastore optimized for similarity search over embeddings, enabling semantic retrieval at scale.
A high-capacity language model trained on massive corpora, exhibiting broad generalization and emergent behaviors.
Fine-tuning on (prompt, response) pairs to align a model with instruction-following behaviors.
Ensuring model behavior matches human goals, norms, and constraints, including reducing harmful or deceptive outputs.
Automated detection/prevention of disallowed outputs (toxicity, self-harm, illegal instruction, etc.).
Techniques to understand model decisions (global or local), important in high-stakes and regulated settings.
Studying internal mechanisms or input influence on outputs (e.g., saliency maps, SHAP, attention analysis).
Local surrogate explanation method approximating model behavior near a specific input.
Framework for reasoning about cause-effect relationships beyond correlation, often using structural assumptions and experiments.
A hidden variable influences both cause and effect, biasing naive estimates of causal impact.
Protecting data during network transfer and while stored; essential for ML pipelines handling sensitive data.
Artificially created data used to train/test models; helpful for privacy and coverage, risky if unrealistic.
Processes and controls for data quality, access, lineage, retention, and compliance across the AI lifecycle.
Systematic review of model/data processes to ensure performance, fairness, security, and policy compliance.
Central system to store model versions, metadata, approvals, and deployment state.
Logging hyperparameters, code versions, data snapshots, and results to reproduce and compare experiments.
Ability to replicate results given same code/data; harder in distributed training and nondeterministic ops.
Time from request to response; critical for real-time inference and UX.
How many requests or tokens can be processed per unit time; affects scalability and cost.
Hardware resources used for training/inference; constrained by memory bandwidth, FLOPs, and parallelism.
Reducing numeric precision of weights/activations to speed inference and reduce memory with acceptable accuracy loss.
A dataset + metric suite for comparing models; can be gamed or misaligned with real-world goals.
System for running consistent evaluations across tasks, versions, prompts, and model settings.
Stress-testing models for failures, vulnerabilities, policy violations, and harmful behaviors before release.
Methods to protect model/data during inference (e.g., trusted execution environments) from operators/attackers.
Error due to sensitivity to fluctuations in the training dataset.
Measures divergence between true and predicted probability distributions.
Measures how one probability distribution diverges from another.
Updating beliefs about parameters using observed evidence and prior distributions.
Estimating parameters by maximizing likelihood of observed data.