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Description
Dropbear is a compact SSH server and client that operates on various Unix-like platforms. It is an open-source program released under an MIT-style license, making it accessible for developers. Its design is particularly advantageous for "embedded" Linux systems, like those found in wireless routers. For those interested in staying updated on new releases or engaging in discussions, a low-traffic mailing list is available for subscriptions. With an efficient memory footprint, Dropbear can be compiled into a statically linked binary of just 110kB using uClibc on x86 architecture, provided that only the essential options are selected. Additionally, the server supports X11 forwarding and authentication-agent forwarding for clients using OpenSSH. Users can compile the server, client, key generator, and key converter into a single executable, similar to busybox, with the ability to disable certain features during compilation to conserve space. The software also includes a multi-hop mode that allows SSH TCP forwarding, enabling users to tunnel through multiple SSH hosts seamlessly in a single command, demonstrating its versatility in various networking scenarios. This flexibility makes Dropbear a favored choice for projects requiring lightweight and efficient SSH solutions.
Description
The Microsoft Cognitive Toolkit (CNTK) is an open-source framework designed for high-performance distributed deep learning applications. It represents neural networks through a sequence of computational operations organized in a directed graph structure. Users can effortlessly implement and integrate various popular model architectures, including feed-forward deep neural networks (DNNs), convolutional neural networks (CNNs), and recurrent neural networks (RNNs/LSTMs). CNTK employs stochastic gradient descent (SGD) along with error backpropagation learning, enabling automatic differentiation and parallel processing across multiple GPUs and servers. It can be utilized as a library within Python, C#, or C++ applications, or operated as an independent machine-learning tool utilizing its own model description language, BrainScript. Additionally, CNTK's model evaluation capabilities can be accessed from Java applications, broadening its usability. The toolkit is compatible with 64-bit Linux as well as 64-bit Windows operating systems. For installation, users have the option of downloading pre-compiled binary packages or building the toolkit from source code available on GitHub, which provides flexibility depending on user preferences and technical expertise. This versatility makes CNTK a powerful tool for developers looking to harness deep learning in their projects.
API Access
Has API
API Access
Has API
Integrations
AI Skills Navigator
Alteryx
AssurX
AuraQuantic
Azure Data Science Virtual Machines
Azure Database for MariaDB
FreeBSD
Microsoft Dynamics 365 Finance
Microsoft Dynamics Supply Chain Management
Microsoft Power Platform
Integrations
AI Skills Navigator
Alteryx
AssurX
AuraQuantic
Azure Data Science Virtual Machines
Azure Database for MariaDB
FreeBSD
Microsoft Dynamics 365 Finance
Microsoft Dynamics Supply Chain Management
Microsoft Power Platform
Pricing Details
Free
Free Trial
Free Version
Pricing Details
No price information available.
Free Trial
Free Version
Deployment
Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook
Deployment
Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook
Customer Support
Business Hours
Live Rep (24/7)
Online Support
Customer Support
Business Hours
Live Rep (24/7)
Online Support
Types of Training
Training Docs
Webinars
Live Training (Online)
In Person
Types of Training
Training Docs
Webinars
Live Training (Online)
In Person
Vendor Details
Company Name
Matt Johnston
Country
Australia
Website
matt.ucc.asn.au/dropbear/dropbear.html
Vendor Details
Company Name
Microsoft
Founded
1975
Country
United States
Website
docs.microsoft.com/en-us/cognitive-toolkit/
Product Features
Product Features
Deep Learning
Convolutional Neural Networks
Document Classification
Image Segmentation
ML Algorithm Library
Model Training
Neural Network Modeling
Self-Learning
Visualization
