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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. It is based on the Structured State Space sequence (S4) model.
Keras is an open-source library that provides a Python interface for artificial neural networks. Keras was first independent software, then integrated into the TensorFlow library, and later supporting more. "Keras 3 is a full rewrite of Keras [and can be used] as a low-level cross-framework language to develop custom components such as layers ...
Caffe (Convolutional Architecture for Fast Feature Embedding) is a deep learning framework, originally developed at University of California, Berkeley. It is open source, under a BSD license. [4] It is written in C++, with a Python interface. [5]
Free and open-source software portal; Shogun is a free, open-source machine learning software library written in C++. It offers numerous algorithms and data structures for machine learning problems. It offers interfaces for Octave, Python, R, Java, Lua, Ruby and C# using SWIG.
The models and the code were released under Apache 2.0 license on GitHub. [4] An individual Inception module. On the left is a standard module, and on the right is a dimension-reduced module. A single Inception dimension-reduced module. The Inception v1 architecture is a deep CNN composed of 22 layers. Most of these layers were "Inception modules".
In machine learning, kernel machines are a class of algorithms for pattern analysis, whose best known member is the support-vector machine (SVM). These methods involve using linear classifiers to solve nonlinear problems. [ 1 ]
Although the Python interface is more polished and the primary focus of development, PyTorch also has a C++ interface. [14] A number of pieces of deep learning software are built on top of PyTorch, including Tesla Autopilot, [15] Uber's Pyro, [16] Hugging Face's Transformers, [17] PyTorch Lightning, [18] [19] and Catalyst. [20] [21]
Multiple kernel learning refers to a set of machine learning methods that use a predefined set of kernels and learn an optimal linear or non-linear combination of kernels as part of the algorithm. Reasons to use multiple kernel learning include a) the ability to select for an optimal kernel and parameters from a larger set of kernels, reducing ...