Implementing Multiple Layer Neural Network from Scratch
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Updated
Apr 14, 2016 - Python
Implementing Multiple Layer Neural Network from Scratch
(Spring 2017) Assignment 2: GPU Executor
Assignment 1: automatic differentiation
Computational graph library for machine learning
A computation graph micro-framework providing seamless lazy and concurrent evaluation
Model-based Policy Gradients
Projeto de TCC baseado em processamento de imagens e visão computacional para identificar a evolução de ogras da engenharia civil
This repository contains the code that produces the numeric section in On the Use of TensorFlow Computation Graphs in combination with Distributed Optimization to Solve Large-Scale Convex Problems
Python library for developing data processing algorithms as computational graphs and their integration with publish-subscribe systems
Implementation of automatic differentiation (AD) in forward and backward modes with mathematical explanations
Real-time execution and remote monitoring and tuning of BDSim Block-Diagrams for modeling and control of Dynamical Systems
GenCoG: A DSL-Based Approach to Generating Computation Graphs for TVM Testing (ISSTA‘23)
artifax is a Python package to evaluate nodes in a computation graph where the dependencies associated with each node are extracted directly from their function signatures.
Jaxpr Visualisation Tool
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