Inception Network
An Inception Network is a type of neural network architecture designed for image recognition tasks, featuring multiple convolutional filters of different sizes in parallel.
Inference Latency
Inference latency is the time it takes for a machine learning model to process input data and produce an output.
Instance Normalization
Instance Normalization is a technique used in machine learning to normalize the input features of each instance individually to improve the stability and performance of neural networks.
Integrated Gradients
Integrated Gradients is an attribution method used in machine learning to understand the contribution of each feature to a model's prediction.
JAX
JAX is a numerical computing library from Google that enables high-performance machine learning through automatic differentiation and just-in-time compilation.
Keras
Keras is an open-source neural network library written in Python that simplifies the creation and training of deep learning models.
Knowledge Distillation
Knowledge Distillation is a machine learning technique where a smaller model learns to mimic a larger model, effectively transferring knowledge.
Kubeflow
Kubeflow is an open-source platform designed to simplify the deployment, management, and scaling of machine learning models on Kubernetes.
Label Smoothing
Label Smoothing is a regularization technique used in machine learning to make model predictions more robust by softening the target labels.
LaMDA
LaMDA is a conversational AI model developed by Google designed to generate more natural and engaging dialogue.
Layer Normalization
Layer normalization is a technique used in neural networks to stabilize and accelerate the training process by normalizing the inputs across the features of each layer.
Leaky ReLU
Leaky ReLU is a type of activation function used in artificial neural networks that allows a small, non-zero gradient when the unit is not active.
LeNet
LeNet is a pioneering convolutional neural network (CNN) architecture developed for image classification tasks.
Listen Attend and Spell
Listen, Attend and Spell Short Definition: Listen, Attend and Spell is a neural network model designed for automatic speech recognition tasks.
Log-Cosh Loss
Log-Cosh Loss is a smooth loss function used in regression tasks to measure the difference between predicted and actual values.
LogSoftmax
LogSoftmax is a mathematical function used in machine learning that applies the log function to the softmax output for numerical stability.
Long Short-Term Memory
Long Short-Term Memory (LSTM) is a type of recurrent neural network (RNN) architecture designed to effectively capture and learn long-range dependencies in sequential data.
Loss Function
A loss function is a mathematical formula that measures how far an AI model’s predictions are from the correct answers.
Lovasz Loss
Lovasz Loss is a surrogate loss function designed for optimizing the intersection-over-union (IoU) metric in machine learning models, particularly for image segmentation tasks.
MarianMT
MarianMT is a neural machine translation model designed for high-quality, multilingual text translation tasks.
Mask R-CNN
Mask R-CNN is a deep learning model designed for object detection and instance segmentation in images.
Matching Networks
Matching Networks are a type of neural network architecture designed for few-shot learning, where the model learns to classify new examples with only a few training instances.
Max Pooling
Max pooling is a down-sampling technique used in convolutional neural networks (CNNs) that selects the maximum value from a feature map region.
mBERT
mBERT is a multilingual version of the BERT model designed to process and understand multiple languages in natural language processing tasks.
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