Deep Learning Algorithms implemented from scratch using Numpy
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Updated
Aug 25, 2019 - Python
Deep Learning Algorithms implemented from scratch using Numpy
My Implementation of well known DL architectures using PyTorch
Tensorflow, Template, Classic NN Architecture
A small collection of basic neural networks for different tasks using pytorch.
Unofficial code with the paper "On the Role of Text Preprocessing in Neural Network Architectures" for IMDb dataset.
Many Neural Network architectures are there. Basically Keras applications. You can find here the structures, implementations all you need. Have fun!
Public repository of our work in the search for an optimal multi-view crop classifier (considering encoder architectures and fusion strategies)
TorchArc: Build PyTorch networks by specifying architectures.
Transformers-based Neural Network harbor logistic prediction model
AAAI 2021. Neural Sequence-to-grid Module for Learning Symbolic Rules
Step by Step Math Behind Multilayer Perceptron Neural Networks Backpropagation with Manual Code Python and Excel For Detecting Potential Obesity
Improving Prediction of Daily Visits of Wikipedia Mathematics Topics using Graph Neural Networks
A toolkit for training CNN-1DRNN-CTC model to perform line-level Handwritten Text Recognition
This repository is the implementation of several famous convolution neural network architecture with Keras. (Resnet v1, Resnet v2, Inception v1/GoogLeNet, Inception v2, Inception v3))
📺 A Python library for pruning and visualizing Keras Neural Networks' structure and weights
[arXiv'18] Security Analysis of Deep Neural Networks Operating in the Presence of Cache Side-Channel Attacks
ActTensor: Activation Functions for TensorFlow. https://pypi.org/project/ActTensor-tf/ Authors: Pouya Ardehkhani, Pegah Ardehkhani
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