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Description
The NVIDIA Deep Learning GPU Training System (DIGITS) empowers engineers and data scientists by making deep learning accessible and efficient. With DIGITS, users can swiftly train highly precise deep neural networks (DNNs) tailored for tasks like image classification, segmentation, and object detection. It streamlines essential deep learning processes, including data management, neural network design, multi-GPU training, real-time performance monitoring through advanced visualizations, and selecting optimal models for deployment from the results browser. The interactive nature of DIGITS allows data scientists to concentrate on model design and training instead of getting bogged down with programming and debugging. Users can train models interactively with TensorFlow while also visualizing the model architecture via TensorBoard. Furthermore, DIGITS supports the integration of custom plug-ins, facilitating the importation of specialized data formats such as DICOM, commonly utilized in medical imaging. This comprehensive approach ensures that engineers can maximize their productivity while leveraging advanced deep learning techniques.
Description
TFlearn is a flexible and clear deep learning framework that operates on top of TensorFlow. Its primary aim is to offer a more user-friendly API for TensorFlow, which accelerates the experimentation process while ensuring complete compatibility and clarity with the underlying framework. The library provides an accessible high-level interface for developing deep neural networks, complete with tutorials and examples for guidance. It facilitates rapid prototyping through its modular design, which includes built-in neural network layers, regularizers, optimizers, and metrics. Users benefit from full transparency regarding TensorFlow, as all functions are tensor-based and can be utilized independently of TFLearn. Additionally, it features robust helper functions to assist in training any TensorFlow graph, accommodating multiple inputs, outputs, and optimization strategies. The graph visualization is user-friendly and aesthetically pleasing, offering insights into weights, gradients, activations, and more. Moreover, the high-level API supports a wide range of contemporary deep learning architectures, encompassing Convolutions, LSTM, BiRNN, BatchNorm, PReLU, Residual networks, and Generative networks, making it a versatile tool for researchers and developers alike.
API Access
Has API
API Access
Has API
Pricing Details
No price information available.
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
NVIDIA DIGITS
Founded
1993
Country
United States
Website
developer.nvidia.com/digits
Vendor Details
Company Name
TFLearn
Website
tflearn.org
Product Features
Deep Learning
Convolutional Neural Networks
Document Classification
Image Segmentation
ML Algorithm Library
Model Training
Neural Network Modeling
Self-Learning
Visualization
Product Features
Deep Learning
Convolutional Neural Networks
Document Classification
Image Segmentation
ML Algorithm Library
Model Training
Neural Network Modeling
Self-Learning
Visualization