DQN
The Deep Q-Network (DQN) is a model-free, off-policy reinforcement learning algorithm that combines Q-learning with a deep neural network function approximator, learning to act directly from raw pixels.
Deep Learning
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Showing 61-120 of 333 articles
Data Augmentation
Data augmentation is a set of techniques that artificially expand the size and diversity of a training dataset by applying label-preserving transformations to existing examples, rather than collecting new…
Data Parallelism
Data parallelism is a distributed training technique in which the same neural network model is replicated across multiple processing units (typically GPUs), each device trains on a different shard of the input…
Dataset API (tf.data)
The Dataset API (tf.data) is the high-performance input pipeline framework within TensorFlow for loading, transforming, and delivering data to machine learning models during training and evaluation.
DeBERTa
DeBERTa (Decoding-enhanced BERT with Disentangled Attention) is a family of pre-trained language models developed by Microsoft Research that improves BERT and RoBERTa with two innovations: a disentangled…
Decoder
A decoder is the component of a neural network that turns an internal, compressed, or abstract representation into a desired output, such as a translated sentence, a generated image, a reconstructed input, or…
Deep Learning
Deep learning is a subset of machine learning that uses artificial neural networks with multiple layers to automatically learn representations of data at multiple levels of abstraction
Deep Model
A deep model, also called a deep learning model or deep neural network, is an artificial neural network built from many stacked layers of processing units that automatically learns hierarchical representations…
Deep Neural Network
A deep neural network (DNN) is an artificial neural network with multiple hidden layers of artificial neurons stacked between its input and output layers
Deep Q-Network (DQN)
Deep Q-Network (DQN) is a reinforcement learning algorithm that uses a deep neural network to approximate the optimal action-value function (Q-function)
DeepLIFT
DeepLIFT (Deep Learning Important FeaTures) is a feature attribution method for deep neural networks introduced by Avanti Shrikumar, Peyton Greenside, and Anshul Kundaje at Stanford University in 2017 .
DeepLab
DeepLab is a family of deep convolutional neural network architectures for semantic segmentation, developed by Liang-Chieh Chen and collaborators at UCLA and Google between 2014 and 2018 .
DeepNorm / DeepNet
DeepNorm is a normalization and weight initialization scheme for Transformer networks that makes the training of very deep models stable.
DeepSeek Sparse Attention (DSA)
DeepSeek Sparse Attention (DSA) is a trainable, fine-grained sparse attention mechanism introduced by the Chinese AI company DeepSeek in its experimental model DeepSeek-V3.2-Exp, released on September 29, 2025.
DeepSpeed
DeepSpeed is an open-source deep learning optimization library, originally developed by Microsoft, that makes distributed training and inference of large models efficient, easy to use, and cost-effective.
DeiT
DeiT (Data-efficient Image Transformers) is a family of vision transformer models that proved Vision Transformers can be trained to state-of-the-art image classification accuracy on ImageNet alone
Denoising
Denoising is the process of removing unwanted noise from data to recover a cleaner underlying signal
Dense Layer
A dense layer, also called a fully connected (FC) layer, linear layer, or affine layer, is a layer in an artificial neural network where every input neuron is connected to every output neuron.
DenseNet
DenseNet (Densely Connected Convolutional Networks) is a convolutional neural network architecture that connects every layer to every other layer in a feed-forward fashion
Depth estimation
Depth estimation is the computer vision task of predicting how far each surface in a scene is from the camera, producing a dense per-pixel depth map from one or more images.
Diederik Kingma
Diederik Kingma is a Dutch machine learning researcher and a founding member of OpenAI who is best known as the first author of the Adam optimizer and the variational autoencoder (VAE)
Diffusion Forcing
Diffusion Forcing is a training paradigm for sequence generative modeling introduced in 2024 that assigns each token in a sequence its own independent, randomly sampled noise level during training .
Diffusion model
A diffusion model is a generative model that learns to transform samples from a simple reference distribution into samples resembling a data distribution by reversing a gradual corruption process.
Diffusion policy
Diffusion policy is a robot imitation-learning method, introduced in 2023 by Cheng Chi, Shuran Song, and collaborators at Columbia University, the Toyota Research Institute, and MIT
Direct Preference Optimization (DPO)
Direct Preference Optimization (DPO) is a method for aligning large language models with human preferences that replaces the multi-stage reinforcement learning from human feedback (RLHF) pipeline with a single…
DistilBERT
DistilBERT is a compressed version of BERT released by Hugging Face in October 2019 that is 40% smaller and 60% faster than BERT-base while retaining 97% of its language-understanding performance on the GLUE…
Double Descent
Double descent is a phenomenon in machine learning and statistical learning theory in which a model's test error, plotted against increasing model complexity, first traces the classical U-shaped bias-variance…
Downsampling
Downsampling is the process of reducing the number of samples, the spatial resolution, or the number of data instances in a signal, image, or dataset in order to lower computational cost and memory use while…
DreamBooth
DreamBooth is a subject-driven fine-tuning method for text-to-image diffusion models that personalizes a pretrained model to a specific subject, for example a particular dog, toy, or person, from just 3 to 5…
Dropout
Dropout is a regularization technique for neural networks that randomly sets a fraction of neuron activations to zero during training
Dropout Regularization
Dropout regularization is a regularization technique for neural networks that prevents overfitting by randomly setting a fraction of neuron activations to zero on each training step, forcing the network to…
ELECTRA
ELECTRA, which stands for Efficiently Learning an Encoder that Classifies Token Replacements Accurately
Eager Execution
Eager execution is an imperative, define-by-run mode of running machine learning framework operations in which each operation is evaluated immediately as it is called and returns a concrete value
Early Stopping
Early stopping is a regularization technique that halts the training of an iterative machine learning model as soon as its performance on a held-out validation set stops improving
EfficientNet
EfficientNet is a family of convolutional neural network architectures and a model-scaling method that uniformly scales network depth, width, and input resolution with a single compound coefficient, developed…
Embedding Space
An embedding space is a continuous, typically high-dimensional vector space in which data objects (words, sentences, images, users, audio clips, code, or other entities) are represented as dense numerical…
Embeddings
In machine learning, an embedding is a learned representation that maps an input into a vector, usually so that a downstream model or comparison rule can use relationships encoded in the vector's geometry.
Embodied AI
Embodied AI is an area of artificial intelligence concerned with agents whose perception, action, learning, and reasoning are coupled through interaction with an environment.
Encoder
An encoder in machine learning is a neural network component that transforms input data (text, an image, audio, or code) into a compressed, structured representation, often called a latent representation…
Epoch
An epoch in machine learning is one complete pass through the entire training dataset, during which every example is presented to the model exactly once to compute gradients and update parameters before the…
Estimator (tf.estimator)
tf.estimator is a high-level TensorFlow API that encapsulates the complete lifecycle of a machine learning model, including training, evaluation, prediction, and export for serving.
Expert Choice routing
Expert Choice routing (often abbreviated EC) is a routing method for mixture of experts (MoE) layers in neural networks, introduced by researchers at Google in 2022.
Exploding Gradient Problem
The exploding gradient problem is a training failure in deep neural networks where the gradients of the loss function grow exponentially large as they propagate backward through layers during backpropagation…
Faster R-CNN
Faster R-CNN is a two-stage object detection model introduced by Shaoqing Ren, Kaiming He, Ross Girshick, and Jian Sun in the 2015 NeurIPS paper Faster R-CNN: Towards Real-Time Object Detection with Region…
Federated Learning
Federated learning is a machine learning technique that trains a shared model across many decentralized devices or servers without moving their raw data to a central location.
Feedforward Neural Network (FFN)
A feedforward neural network (FFN), also called a multilayer perceptron (MLP) when it has multiple layers, is a type of artificial neural network in which information flows in one direction only, from the…
Few-Shot Learning
Few-shot learning is a branch of machine learning in which a model learns to recognize new classes or perform new tasks from only a very small number of labeled examples, typically one to five per class .
Fine Tuning
Fine-tuning is the process of adapting a pretrained machine-learning model by continuing to optimize some or all of its parameters on data selected for a target task, domain, behavior, or population.
Focal loss
Focal loss is a loss function that reshapes standard cross-entropy loss by adding a (1 - p_t)^gamma modulating factor, which down-weights well-classified (easy) examples so that training concentrates on hard
François Chollet
François Chollet (born 20 October 1989) is a French software engineer and artificial intelligence researcher best known as the creator of the Keras deep-learning library, the originator of the Abstraction and…
Full Softmax
Full softmax (also called the standard softmax or exact softmax) is the softmax computation that calculates a probability for every possible output class in a classification model
Fully Connected Layer
A fully connected layer (also called a dense layer or linear layer) is a layer in an artificial neural network in which every input value connects to every output value through a learned weight
Fully Sharded Data Parallel (FSDP)
Fully Sharded Data Parallel (FSDP) is a distributed training technique implemented in PyTorch that shards a model's parameters, gradients, and optimizer states across data-parallel workers, allowing models…
GAN
GAN stands for generative adversarial network, a class of deep learning generative models in which two neural networks are trained against each other: a generator that fabricates synthetic data and a…
GELU (Gaussian Error Linear Unit)
The Gaussian Error Linear Unit (GELU) is a smooth, non-monotonic activation function defined as GELU(x) = x · Φ(x), where Φ(x) is the cumulative distribution function of the standard normal distribution.
GNoME
GNoME (Graph Networks for Materials Exploration) is a deep-learning system from Google DeepMind that predicts the thermodynamic stability of inorganic crystals and uses those predictions to search for new…
GPTQ
GPTQ (Generative Pre-trained Transformer Quantization) is a one-shot post-training quantization method that compresses the weights of large language models to 3 or 4 bits using approximate second-order…
GPU computing
GPU computing is the use of a graphics processing unit (GPU) to perform general-purpose computation that was traditionally handled by the central processing unit (CPU).
Gated DeltaNet
Gated DeltaNet is a linear attention sequence model that augments the delta rule, a key-value error-correction update used in the earlier DeltaNet architecture
Gaussian Splatting
Gaussian splatting is a method for real-time radiance field rendering that represents a 3D scene as a collection of millions of anisotropic 3D Gaussian primitives, each defined by a position, covariance…