Search Results for "q-learning"
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Showing 26 open source projects for "q-learning"
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Build Securely on AWS with Proven FrameworksMoving to the cloud brings new challenges. How can you manage a larger attack surface while ensuring great network performance? Turn to Fortinet’s Tested Reference Architectures, blueprints for designing and securing cloud environments built by cybersecurity experts. Learn more and explore use cases in this white paper.
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gtop
System monitoring dashboard for terminal
gtop is a tool that helps you monitor your systems through a dashboard user interface, and it is optimized to monitor and visualize terminals. gtop requirements are to have Node.js version 8 or higher. It supports Linux, OSX, and Windows (with partial support). You need to assign host net and pid to access the metrics in the host machine. Start gtop with the gtop command. To stop gtop use q, or ctrl+c in most shell environments. You can sort the process table by pressing 'p' for Process Id, 'c... -
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Nvitop
An interactive NVIDIA-GPU process viewer and beyond
nvitop is an interactive NVIDIA device and process monitoring tool. It has a colorful and informative interface that continuously updates the status of the devices and processes. As a resource monitor, it includes many features and options, such as tree-view, environment variable viewing, process filtering, process metrics monitoring, etc. Beyond that, the package also ships a CUDA device selection tool nvisel for deep learning researchers. It also provides handy APIs that allow developers... -
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HertzBeat
An open source, real-time monitoring system with custom-monitoring
An open-source, real-time monitoring system with custom monitoring, performance-cluster, Prometheus-compatible and agentless. [Monitoring+Alarm+Notification] all in one. Support monitoring web service, database, os, middleware, cloud-native, network and more. Easy to use and agentless, offering full web-based operations for monitoring and alerting with just a few clicks. Makes protocols such as Http, Jmx, Ssh, Snmp, Jdbc configurable, allowing you to collect any metrics by simply configuring... -
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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. It appears neither CLIP nor prior... -
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manticoresearch
Easy to use open source fast database for search
Manticore Search is an easy to use open source fast database for search. Modern MPP architecture and smart query parallelization capabilities allow to fully utilize all your CPU cores to lower response time as much as possible, when needed. Powerful and fast full-text searching which works fine for small and big datasets. Columnar storage support via the Manticore Columnar Library for bigger datasets (much bigger than can fit in RAM). SQL-first: Manticore's native syntax is SQL. It speaks... -
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PyG
Graph Neural Network Library for PyTorch
PyG (PyTorch Geometric) is a library built upon PyTorch to easily write and train Graph Neural Networks (GNNs) for a wide range of applications related to structured data. It consists of various methods for deep learning on graphs and other irregular structures, also known as geometric deep learning, from a variety of published papers. In addition, it consists of easy-to-use mini-batch loaders for operating on many small and single giant graphs, multi GPU-support, DataPipe support, distributed... -
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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... -
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CLIP-as-service
Embed images and sentences into fixed-length vectors
CLIP-as-service is a low-latency high-scalability service for embedding images and text. It can be easily integrated as a microservice into neural search solutions. Serve CLIP models with TensorRT, ONNX runtime and PyTorch w/o JIT with 800QPS[*]. Non-blocking duplex streaming on requests and responses, designed for large data and long-running tasks. Horizontally scale up and down multiple CLIP models on single GPU, with automatic load balancing. Easy-to-use. No learning curve, minimalist design... -
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Syslogd2
High capacity syslog data collection, filtering, and management.
Syslogd2 is a syslog daemon that has been completely re-imagined specifically for use in network environments. It is multi-threaded, scalable and versatile with features designed for both network and host managers. Each Syslogd2 binary is customized from a set of over 20 features at compile-time. It can support input from text files, named-pipes, Linux kernel and user-defined Linux and (both IPv4 and IPv6) IP sockets (both UDP and TCP). It provides a pre-loadable name-cache that can... -
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Zenoss Community Edition
Zenoss - Intelligent IT Operations Management
Zenoss provides software-defined IT operations for the world’s largest organizations. We deliver the ultimate level of IT service health with simplicity by providing the most granular and intelligent IT service modeling possible, at any scale, and sharing these unique insights with other IT operations management (ITOM) tools to make them more efficient. Zenoss Community Edition is not a “demo” or trial version of Zenoss Enterprise or Zenoss Cloud! Before You install Zenoss Community... -
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ESP8266 Deauther Version 2
Affordable WiFi hacking platform for testing and learning
Scan for WiFi devices, block selected connections, create dozens of networks and confuse WiFi scanners! Version 3 is in development. It is stable to use, but it is very different. It is command-line based, which allows it to offer not just more features, but make them more customizable. This software allows you to easily perform a variety of actions to test 802.11 wireless networks by using an inexpensive ESP8266 WiFi SoC (System On A Chip). The main feature, the deauthentication attack, is... -
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Keras TCN
Keras Temporal Convolutional Network
TCNs exhibit longer memory than recurrent architectures with the same capacity. Performs better than LSTM/GRU on a vast range of tasks (Seq. MNIST, Adding Problem, Copy Memory, Word-level PTB...). Parallelism (convolutional layers), flexible receptive field size (possible to specify how far the model can see), stable gradients (backpropagation through time, vanishing gradients). The usual way is to import the TCN layer and use it inside a Keras model. The receptive field is defined as the... -
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LaPath
Learning Automata algorithm for the shortest path problem.
The shortest path problem is solved by many methods. Heuristics offer lower complexity in expense of accuracy. There are many use cases where the lower accuracy is acceptable in return of lower consumption of computing resources. Learning Automata (LA) are adaptive mechanisms requiring feedback from the executing environment to converge to certain states. In the context of network routing, LA residing at intermediate nodes along a path, exploit feedback from the destination node for reducing... -
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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. Please set the margin argument to 0 for tight cropping. You can evaluate a trained... -
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FastoCloud PRO
IPTV/NVR/CCTV/Video cloud https://fastocloud.com
... Mozaic Many Outputs Physical Inputs Streaming Protocols File Formats Presets Vods/Series server-side support Pay per view channels Channels on demand HTTP Live Streaming (HLS) server-side support Public API, client server communication via JSON RPC Protocol gzip compression Deep learning video analysis Supported deep learning frameworks: Tensorflow NCSDK Caffe ML Hardware: -
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Why Can't I Connect?
Makes it easier to resolve TCP/IP connection errors.
"Why Can't I Connect?" makes it easier to resolve TCP/IP connection errors. Use it to diagnose connections to common server types and create generic clients and / or servers. A source tar, a 32 bit and 64 bit rpm, a 32 bit, 64 bit, and Raspbian / armhf deb, and a 32 bit Windows install exe are available. The 32 bit Windows executable runs fine in 64 bit Windows systems. Go to: https://www.whycanticonnect.com/?q=instructions for help / instructions. Go to: https://www.whycanticonnect.com/?q... -
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Deep Learning with Keras and Tensorflow
Introduction to Deep Neural Networks with Keras and Tensorflow
Introduction to Deep Neural Networks with Keras and Tensorflow. To date tensorflow comes in two different packages, namely tensorflow and tensorflow-gpu, whether you want to install the framework with CPU-only or GPU support, respectively. NVIDIA Drivers and CuDNN must be installed and configured before hand. Please refer to the official Tensorflow documentation for further details. Since version 0.9 Theano introduced the libgpuarray in the stable release (it was previously only available in... -
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VBS for Research on the Internet
Welcome to the Volunteer-Based System for Research on the Internet!
... by different applications or different groups of applications - creating a traffic generator, to imitate traffic generated by particular applications, or to imitate the real traffic in the network - implementing smart assessment of QoS in the network at the users' level and in the core of the network - obtaining precise data needed to train Machine Learning Algorithms - many more cases :-) -
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NiceShaper - Dynamic Traffic Shaper
NiceShaper provides dynamic traffic shaping for Linux router
NiceShaper is the program developed for Linux router environment. It works in user space on top of standard Linux QOS implementation and iptables. By default, a proven HTB algorithm is used for the root, inner, and leaf classes, SFQ packets scheduling algorithm is the default queuing discipline (qdisc) contained within each of leaf classes, U32 and FW are used as the packets classifiers. NiceShaper provides dynamic traffic shaping approach which is more effective than traditional shaping... -
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FlowDoh
Dashboard of network top-talkers using netflow sources
... file-sharing) * Create a baseline of network statistics about your servers (planned feature) FAQ: Q: Why is it called FlowDoh? A: Because "'Doh!" is what you will say after installing FlowDoh and finding that some of your servers are already hacked. Another possible answer is this acronym: Flow-Dashboard-Of-Hogs. -
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Raddle is a network emulation framework. It is particularly useful when testing network management systems or teaching people to use them. Raddle supports ping, SNMP, traceroute, and other protocols.
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TNV visualizes pcap data to graphically depict network packets, links, and ports for network traffic analysis to facilitate learning what constitutes 'normal' behavior, investigating security events, or network troubleshooting.
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A packet dissector driven by machine learning algorithms. You train it to recognize specific types of packets by showing it examples and counterexamples of some packet type, and it will figure out which bits in the packet define it as the type you seek.
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A network simulator written in flash. Build up a topolog and send packets throught your network, see and inspect them as they travel, change the headers and observe such protocols as ARP and switch learning.
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WHEREAMI is an intelligent and self-learning network detection and configuration utility. With this capability, a user can just walk to a network range and WHEREAMI will self determine and connect to that network with relevant policies.