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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 ...
A model describes how units of computations, memories, and communications are organized. [1] The computational complexity of an algorithm can be measured given a model of computation. Using a model allows studying the performance of algorithms independently of the variations that are specific to particular implementations and specific technology.
Model compression (e.g. quantization and pruning of model parameters) can be applied to a deep neural network after it has been trained. [19] In the SqueezeNet paper, the authors demonstrated that a model compression technique called Deep Compression can be applied to SqueezeNet to further reduce the size of the parameter file from 5 MB to 500 ...
To enable handling long data sequences, Mamba incorporates the Structured State Space sequence model (S4). [2] S4 can effectively and efficiently model long dependencies by combining continuous-time, recurrent, and convolutional models. These enable it to handle irregularly sampled data, unbounded context, and remain computationally efficient ...
TensorFlow serves as a core platform and library for machine learning. TensorFlow's APIs use Keras to allow users to make their own machine-learning models. [33] [43] In addition to building and training their model, TensorFlow can also help load the data to train the model, and deploy it using TensorFlow Serving. [44]
Self-contained DNN Model Pre-processing and Post-processing Run-time configuration for tuning & calibration DNN model interconnect Common platform TensorFlow, Keras, Caffe, Torch: Algorithm training No No / Separate files in most formats No No No Yes ONNX: Algorithm training Yes No / Separate files in most formats No No No Yes
Salesforce, Inc. is an American cloud-based software company headquartered in San Francisco, California.It provides applications focused on sales, customer service, marketing automation, e-commerce, analytics, artificial intelligence, and application development.
In theory, classic RNNs can keep track of arbitrary long-term dependencies in the input sequences. The problem with classic RNNs is computational (or practical) in nature: when training a classic RNN using back-propagation, the long-term gradients which are back-propagated can "vanish", meaning they can tend to zero due to very small numbers creeping into the computations, causing the model to ...