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Home Page: https://fl03.github.io/concision/
License: Apache License 2.0
Concision is a toolkit for building machine-learning models in Rust.
Home Page: https://fl03.github.io/concision/
License: Apache License 2.0
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A neural network is a set of layers that work together in-order to make a prediction.
This trait should unify all numeric operators implemented in the crate and streamline several additional processes.
Create functions that consider Real-Valued inputs.
LecunNormal initializes objects using a truncated normal distribution that is centered at 0 and has a standard deviation of 1/n where n is the number of inputs.
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Layer normalization directly estimates the normalization statistics from the summed inputs to the neurons in a hidden layer.
The implementation can leverage the ParamsBase created for linear models.
TruncatedNormal
is similar to Normal
distributions, however, any values exceeding two times the standard deviation are discarded and regenerated.
lecun_normal
normal
stdnorm
uniform
In the paper, the Annotated S4, they leverage the scan function from jax. While Rust has a well-developed scan feature present in iterators, it seems to differ in execution than the one from jax.
cnc::linear::Params
Currently, concision
is far too clunky and needs to be cleaned up before any additional work can take place.
Artificial neurons are given two free-parameters, bias and weights, that are to be learned through training. The bias describes an array of values that don't require any input to have an affect.
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The casual convolution function currently considers two array's of equal dimensions, however, the generated kernel is defined to be a one-dimensional array by the 'kernel_dplr' function.
XavierNormal
Each weight is drawn from a normal distribution where the mean
XavierUniform
Each weight is drawn from a uniform distribution in
Scaled Dot-Product Attention is a mechanism most extensively leveraged in the popular Transformer model.
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The transformer model was introduced in 2017 with the release of the Attention is all you need paper. Over the last few years, the popularity of the Transformer model has grown exponentially due to its abilities in natural language processing (NLP) workloads.
Transformers are mathematically simply, primarily relying on dot-product / matrix multiplication algorithms.
Currently the rely on the wrong dimension, thus altering the shape.
Due to the lack of generalization present in Rust, it is important to consider exactly what any given model is operating on. Standardizing this would provide the system with the intended flexibility and may even have performance benefits.
A tensor is defined to be an n-dimensional array.
Development of this macro officially kicks off a long-standing desire to create a DSL of-sorts for building powerful, agile, and scalable neural networks.
Use either uuid::Uuid
or another Copy
able type for ids.
Reinforcement learning is a type of machine learning that involves training an agent to take actions that maximize a reward signal.
Description
Implement a complete framework for building reinforcement learning models in pure Rust.
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