Index

A

• addressing mechanisms, Neural Turing Machine (NTM)
  • content-based addressing / Content-based addressing, Copy tasks using NTM
  • location-based addressing / Location-based addressing, Copy tasks using NTM
• Adversarial Meta Learning (ADML)
  • about / Adversarial meta learning, ADML
  • Fast Gradient Sign Method (FGSM) / FGSM
  • building / Building ADML from scratch
  • data points generation / Generating data points
  • FGSM, using / FGSM
  • single layer neural network / Single layer neural network
  • implementing / Adversarial meta learning
• Artificial General intelligence (AGI) / Algorithm
• audio recognition model
  • building, siamese networks used / Building an audio recognition model using siamese networks

C

• CACTUS
  • about / CACTUs
  • task generation / Task generation using CACTUs
• CAML algorithm / CAML algorithm
• classification
  • performing, prototypical networks used / Performing classification using prototypical networks
• components, Neural Turing Machine (NTM)
  • controller / NTM
  • memory / NTM
  • read head / NTM
  • write head / NTM
• concept discrimination loss / Concept discrimination loss
• concept discriminator / Concept discriminator
• concept generator / Learning to learn in concept space, Concept generator
• concept space
  • about / Learning to learn in concept space
  • algorithm / Algorithm
• Context Adaptation for Meta Learning (CAML)
  • about / CAML
  • context parameter / CAML
  • shared parameter / CAML
  • algorithm / CAML algorithm
• Convolutional Neural Network (CNN) / What are siamese networks?

E

• embedding functions, matching networks
  • support set embedding function (g) / The support set embedding function (g)
  • query set embedding function (f) / The query set embedding function (f)
• embeddings / Prototypical networks
• episodic fashion / Meta learning and few-shot

F

• face recognition
  • with siamese networks / Face recognition using siamese networks
• Fast Gradient Sign Method (FGSM) / FGSM
• few-shot learning
  • about / Meta learning and few-shot
  • optimization, as model / Optimization as a model for few-shot learning

G

• Gaussian prototypical network
  • about / Gaussian prototypical network
  • radius component / Gaussian prototypical network
  • diagonal component / Gaussian prototypical network
  • algorithm / Algorithm
• generalized entropy measures / Theil index
• gradient agreement
  • as optimization / Gradient agreement as an optimization
  • weight calculation / Weight calculation
• gradient agreement algorithm
  • about / Algorithm
  • building, with MAML / Building gradient agreement algorithm with MAML
  • data points, generating / Generating data points
  • single layer neural network / Single layer neural network
  • in MAML / Gradient agreement in MAML

I

• inequality measures, TAML
  • about / Inequality measures
  • Gini coefficient / Gini coefficient
  • Theil index / Theil index
  • variance of algorithms / Variance of algorithms

K

• k-shot learning / Meta learning and few-shot

L

• location-based addressing, Neural Turing Machine (NTM)
  • interpolation / Interpolation, Copy tasks using NTM
  • convolution shift / Convolution shift, Copy tasks using NTM
  • sharpening / Sharpening, Copy tasks using NTM

M

• matching networks
  • about / Matching networks
  • embedding functions / Embedding functions
  • architecture / The architecture of matching networks
  • in TensorFlow / Matching networks in TensorFlow
• Mean Squared Error (MSE) / Loss function
• Memory-Augmented Neural Networks (MANN)
  • about / Memory-augmented neural networks (MANN)
  • read operation / Read operation
  • write operation / Write operation
• Meta-SGD
  • about / Meta-SGD
  • for supervised learning / Meta-SGD for supervised learning
  • building / Building Meta-SGD from scratch
  • data points generation / Generating data points
  • single layer neural network / Single layer neural network
  • implementing / Meta-SGD
  • in reinforcement learning / Meta-SGD for reinforcement learning
• Meta Imitation Learning (MIL)
  • about / Meta imitation learning
  • algorithm / MIL algorithm
• meta learner / Meta learner
• meta learning
  • about / Meta learning
  • types / Types of meta learning
  • metric-based meta learning / Learning the metric space
  • initializations-based meta learning / Learning the initializations
  • optimizer-based meta learning / Learning the optimizer
• meta learning algorithms / Learning to learn gradient descent by gradient descent
• meta learning loss / Meta learning loss
• Model Agnostic Meta Learning (MAML)
  • about / MAML
  • algorithm / MAML algorithm
  • in supervised learning / MAML in supervised learning
  • building / Building MAML from scratch
  • data points generation / Generate data points
  • single layer neural networks / Single layer neural network
  • implementing / Training using MAML
  • in reinforcement learning / MAML in reinforcement learning

N

• Neural Turing Machine (NTM)
  • about / NTM
  • components / NTM
  • read operation / Read operation, Copy tasks using NTM
  • write operation / Write operation, Copy tasks using NTM
  • erase operation / Erase operation
  • add operation / Add operation
  • addressing mechanisms / Addressing mechanisms, Copy tasks using NTM
  • tasks, copying / Copy tasks using NTM

O

• one-shot learning / Meta learning and few-shot

P

• prototypical networks
  • about / Prototypical networks
  • example / Prototypical networks
  • flow / Prototypical networks
  • algorithm / Algorithm
  • used, for performing classification / Performing classification using prototypical networks

Q

• query set / Meta learning and few-shot
• query set embedding function (f) / The query set embedding function (f)

R

• rectified linear unit (ReLU) / Face recognition using siamese networks
• Recurrent Neural Network (RNN) / Learning to learn gradient descent by gradient descent
• relation networks
  • about / Relation networks
  • in one-shot learning / Relation networks in one-shot learning
  • in few-shot learning / Relation networks in few-shot learning
  • in zero-shot learning / Relation networks in zero-shot learning
  • loss function / Loss function
  • building, Tensorflow used / Building relation networks using TensorFlow
• Reptile algorithm
  • about / Reptile, The Reptile algorithm
  • sine wave regression / Sine wave regression using Reptile
  • implementing / Reptile

S

• semi prototypical networks / Semi-prototypical networks
• siamese networks
  • about / What are siamese networks?
  • working / What are siamese networks?
  • architecture / Architecture of siamese networks
  • applications / Applications of siamese networks
  • reference / Applications of siamese networks
  • using, in face recognition / Face recognition using siamese networks
  • audio recognition model, building / Building an audio recognition model using siamese networks
• sine wave regression, Reptile
  • about / Sine wave regression using Reptile
  • data points generation / Generating data points
  • two layered neural network / Two-layered neural network
• Stochastic Gradient Descent (SGD) / Reptile
• support set / Meta learning and few-shot, Prototypical networks
• support set embedding function (g) / The support set embedding function (g)

T

• Task-agnostic meta-learning (TAML)
  • about / Task agnostic meta learning (TAML)
  • entropy maximization/reduction / Entropy maximization/reduction
  • working / Algorithm
  • inequality minimization / Inequality minimization
  • implementing / Algorithm
• task generation
  • with CACTUS / Task generation using CACTUs
• tasks
  • copying, Neural Turing Machine (NTM) used / Copy tasks using NTM
• TensorFlow
  • relation networks, building / Building relation networks using TensorFlow
  • matching networks / Matching networks in TensorFlow
• Theil index / Theil index

Z

• zero-shot learning / Meta learning and few-shot