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.
Megatron-Turing NLG
Megatron-Turing NLG is a large-scale natural language generation model developed to understand and generate human-like text.
Memory Network
A Memory Network is a type of neural network architecture designed to improve the ability of models to store and retrieve information over long sequences.
Message Passing Neural Network
A Message Passing Neural Network (MPNN) is a type of neural network architecture designed to learn representations of graph-structured data by propagating messages along the edges of the graph.
Mish Activation
Mish Activation is a type of activation function used in neural networks to enhance learning capabilities by smoothing gradients.
Mixed Precision Training
Mixed Precision Training is a technique in machine learning that combines different numerical precisions to optimize computational efficiency and memory usage without significantly compromising model accuracy.
Mixture of Experts
Mixture of Experts is a machine learning technique that combines multiple models to solve complex problems by assigning different tasks to specialized sub-models.
Mixup
Mixup is a technique used in data augmentation to improve machine learning models by combining multiple data samples.
MLflow
MLflow is an open-source platform designed to manage the machine learning lifecycle, including experimentation, reproducibility, and deployment.
MLP-Mixer
MLP-Mixer is a type of neural network architecture that uses Multi-Layer Perceptrons (MLPs) for both spatial and channel-wise mixing of information.
MobileNet
MobileNet is a family of lightweight deep learning models designed for efficient mobile and embedded vision applications.
Model Checkpointing
Model checkpointing is a process in machine learning where the state of a model is saved at certain intervals during training.
Model Compression
Model compression is the process of reducing the size and complexity of machine learning models while maintaining or improving their performance.
Model Serving
Model serving is the process of deploying a machine learning model into a production environment to make predictions on new data.
Model-Agnostic Meta-Learning
Model-Agnostic Meta-Learning (MAML) is a framework designed for training machine learning models to adapt quickly to new tasks with minimal data.
Multi-Head Attention
Multi-Head Attention is a mechanism used in neural networks to improve performance by allowing the model to focus on different parts of the input sequence simultaneously.
MuZero
MuZero is a state-of-the-art AI algorithm developed by DeepMind that learns to play games and solve complex problems without being explicitly programmed with the rules.
MXNet
MXNet is an open-source deep learning framework that is highly efficient and flexible for training and deploying neural networks.