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Mamba [a] is a deep learning architecture focused on sequence modeling. It was developed by researchers from Carnegie Mellon University and Princeton University to address some limitations of transformer models , especially in processing long sequences.
A software design description (a.k.a. software design document or SDD; just design document; also Software Design Specification) is a representation of a software design that is to be used for recording design information, addressing various design concerns, and communicating that information to the design’s stakeholders.
In machine learning, hyperparameter optimization [1] or tuning is the problem of choosing a set of optimal hyperparameters for a learning algorithm. A hyperparameter is a parameter whose value is used to control the learning process, which must be configured before the process starts. [2] [3]
In machine learning, a neural scaling law is an empirical scaling law that describes how neural network performance changes as key factors are scaled up or down. These factors typically include the number of parameters, training dataset size, [ 1 ] [ 2 ] and training cost.
It also allowed them to transform existing machine learning processes into reproducible DVC pipelines. DVC 0.6 solved most of the common problems that machine learning engineers and data scientists were facing: the reproducibility of machine learning experiments, as well as data versioning and low levels of collaboration between teams.
In early 2000, the software was developed into a client–server model architecture, and shortly afterward, the client front-end interface component was rewritten fully and replaced with a new Java front-end, which allowed deeper integration with the other tools provided by SPSS. SPSS Clementine version 7.0: The client front-end runs under Windows.
TRDDC undertakes research in Machine Learning, Software Engineering, Process Engineering and Systems Research. TRDDC developed TCS Code Generator Framework (formerly called MasterCraft), [1] an artificial intelligence software that can automatically create code from a simple computer language and rewrite the code based on the user's needs. [2]
It includes the Zero Redundancy Optimizer (ZeRO) for training models with 1 trillion or more parameters. [4] Features include mixed precision training, single-GPU, multi-GPU, and multi-node training as well as custom model parallelism. The DeepSpeed source code is licensed under MIT License and available on GitHub. [5]