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The following tables compare notable software frameworks, libraries, and computer programs for deep learning applications. Deep learning software by name [ edit ]
Direct members of this category should be general-purpose software for training or otherwise interacting with deep learning models. Specific deep learning models, or consumer software applications powered by deep learning, should be placed in Category:Deep learning software applications.
The plain transformer architecture had difficulty converging. In the original paper [1] the authors recommended using learning rate warmup. That is, the learning rate should linearly scale up from 0 to maximal value for the first part of the training (usually recommended to be 2% of the total number of training steps), before decaying again.
Deep learning is a subset of machine learning that focuses on utilizing neural networks to perform tasks such as classification, regression, and representation learning.The field takes inspiration from biological neuroscience and is centered around stacking artificial neurons into layers and "training" them to process data.
Apache Mahout, a library of scalable machine learning algorithms. [80] Deeplearning4j, an open-source, distributed deep learning framework written for the JVM. [81] Keras, a high level open-source software library for machine learning (works on top of other libraries). [82]
The book outlines five approaches of machine learning: inductive reasoning, connectionism, evolutionary computation, Bayes' theorem and analogical modelling.The author explains these tribes to the reader by referring to more understandable processes of logic, connections made in the brain, natural selection, probability and similarity judgments.
Torch is an open-source machine learning library, a scientific computing framework, and a scripting language based on Lua. [3] It provides LuaJIT interfaces to deep learning algorithms implemented in C. It was created by the Idiap Research Institute at EPFL. Torch development moved in 2017 to PyTorch, a port of the library to Python. [4] [5] [6]
Deep Learning Studio is a software tool that aims to simplify the creation of deep learning models used in artificial intelligence. [1] It is compatible with a number of open-source programming frameworks popularly used in artificial neural networks , including MXNet and Google's TensorFlow .