Search Results for "neural network training"
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Showing 39 open source projects for "neural network training"
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Imagen - Pytorch
Implementation of Imagen, Google's Text-to-Image Neural Network
Implementation of Imagen, Google's Text-to-Image Neural Network that beats DALL-E2, in Pytorch. It is the new SOTA for text-to-image synthesis. Architecturally, it is actually much simpler than DALL-E2. It consists of a cascading DDPM conditioned on text embeddings from a large pre-trained T5 model (attention network). It also contains dynamic clipping for improved classifier-free guidance, noise level conditioning, and a memory-efficient unit design. -
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PyG
Graph Neural Network Library for PyTorch
...All it takes is 10-20 lines of code to get started with training a GNN model (see the next section for a quick tour). -
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Spektral
Graph Neural Networks with Keras and Tensorflow 2
Spektral is a Python library for graph deep learning, based on the Keras API and TensorFlow 2. The main goal of this project is to provide a simple but flexible framework for creating graph neural networks (GNNs). You can use Spektral for classifying the users of a social network, predicting molecular properties, generating new graphs with GANs, clustering nodes, predicting links, and any other task where data is described by graphs. Spektral implements some of the most popular layers for graph deep learning. Spektral also includes lots of utilities for representing, manipulating, and transforming graphs in your graph deep learning projects. ... -
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ENAS in PyTorch
PyTorch implementation of "Efficient Neural Architecture Search
ENAS in PyTorch is a PyTorch implementation of Efficient Neural Architecture Search (ENAS), a method that automates the design of neural network architectures through reinforcement learning and parameter sharing. The repository demonstrates how a controller network can explore a large search space and discover high-performing architectures while dramatically reducing the computational cost traditionally associated with neural architecture search. ... -
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Age and Gender Estimation
Keras implementation of a CNN network for age and gender estimation
Keras implementation of a CNN network for age and gender estimation. This is a Keras implementation of a CNN for estimating age and gender from a face image [1, 2]. In training, the IMDB-WIKI dataset is used. Because the face images in the UTKFace dataset is tightly cropped (there is no margin around the face region), faces should also be cropped in demo.py if weights trained by the UTKFace dataset is used. -
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DeepCluster
Deep Clustering for Unsupervised Learning of Visual Features
DeepCluster is a classic self-supervised clustering-based representation learning algorithm that iteratively groups image features and uses the cluster assignments as pseudo-labels to train the network. In each round, features produced by the network are clustered (e.g. k-means), and the cluster IDs become supervision targets in the next epoch, encouraging the model to refine its representation to better separate semantic groups. This alternating “cluster & train” scheme helps the model gradually discover meaningful structure without labels. ... -
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Metasploitable
Metasploitable is an intentionally vulnerable Linux virtual machine
This is Metasploitable2 (Linux) Metasploitable is an intentionally vulnerable Linux virtual machine. This VM can be used to conduct security training, test security tools, and practice common penetration testing techniques. The default login and password is msfadmin:msfadmin. Never expose this VM to an untrusted network (use NAT or Host-only mode if you have any questions what that means). To contact the developers, please send email to msfdev@metasploit.com -
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brain.js
GPU accelerated Neural networks in JavaScript for Browsers
...Brain.js is super simple to use. You do not need to know Neural Networks in detail to work with this. Brain.js performs computations using GPU and gracefully fallback to pure JavaScript when GPU is not available. Brain.js provides multiple neural network implementations as different neural nets can be trained to do different things well. Easily export and import trained models using JSON format or as a function. -
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Free Academic Timetable Software
Free web timetabling software for education and training providers.
A user friendly web-based timetabling software designed for all types of education and training providers to schedule classes, facilities, trainers and split classes into groups. It was designed by an academic professional with over 5 years experience in education timetabling systems and 14 years experience in the education and training sector. It is easy to set up and is really user friendly for everyone. Comes with manuals, training videos and is free to use and install, but not for... -
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LAMPSecurity training is designed to be a series of vulnerable virtual machine images along with complementary documentation designed to teach linux,apache,php,mysql security.
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neuranep
Neural Network Engineering Platform
...SpikeOS was oriented towards computational modeling. NeuraNEP is oriented toward neural network research. -
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LensOSX
LensOSX: the light, efficient network simulator
Lens is the light, efficient network simulator, written by Doug Rohde. LensOSX is a native MacOSX port of Lens that runs on MacOSX 10.5 or higher, created by Harm Brouwer, Daniel de Kok and Hartmut Fitz. -
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OSXtlearn
OSXtlearn: tlearn for MacOSX
tlearn is a backpropagation neural network simulator, written by Jeff Elman. xtlearn is a version of tlearn for the X Window System. OSXtlearn is xtlearn wrapped in a MacOSX application bundle that runs ons MacOSX 10.5 or higher and that requires XQuartz. OSXtlearn is created by Harm Brouwer. -
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BPNNet
Backpropagation neural network simulator
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AHANNS
AHANNS - Amateur Homemade Artificial Neural Network Simulator
AHANNS enables the user to create and train artificial neural networks. Training is carried out by an implementation of back propagation learning algorithm. See the documentation for details. This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. -
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TK Neuron Simulator
A neural network simulator
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...If you have problems getting the mac changer to work, try using a mac address that starts with something other than "00" in the first octet. If you are intersted in learning about network security please check out my Juniper Networks training course at: http://academy.gns3.com/courses/juniper-jncia-intro-to-junos
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A Java neural network simulator for a type of new spiking neuron model. More information will be available later.
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E-HRIS Is an open source human resource information management system developed to automate all aspects of human resource management i.e. contract management,leave management,training management. The Project demo available at http://www.e-hris.com
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Joone is a neural net framework written in Java(tm). It's composed by a core engine, a GUI editor and a distributed training environment and can be extended by writing new modules to implement new algorithms or architectures starting from base component
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Geeks Artificial Neural Network (G.A.N.N) is an open source project that started with the philosophy of being a new more advanced A.N.N that works as a platform for other applications. In other words, G.A.N.N should be considered as a "Black Box".
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Cervelletto is a neural network simulator. It uses a new neural model based on biological, neurological and psychological studies. [it's not yet completed... just give me some weeks! sorry!]
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OpenDiscreteDynamicProgrammingTemplate : founds optimal constrainted parameters of a discrete controls with second order optimization template replacing Hessian with directional derivatives and backpropagation for digital filter(as neural network)
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‘intelliworm’ is a prototype simulation of a Intelligent Species of a common earth worm,which inherits human like decision making capabilities through Neural Network intelligence representation,all developed by integrating .NET with Lisp and Yacc.
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This project uses massively parallel Graphics Processing Units(GPU) for neural network(Backpropagation) purposes.
