Software Alternatives, Accelerators & Startups
Register | Login

NVIDIA DIGITS VS Deeplearning4j

Compare NVIDIA DIGITS VS Deeplearning4j and see what are their differences

Momen
Build powerful web apps, and create your custom AI apps, no code needed! featured
Contents:
  1. » Base Details
  2. » Videos
  3. » Reviews
  4. » Alternatives

NVIDIA DIGITS logo NVIDIA DIGITS

DIGITS (the Deep Learning GPU Training System) is a webapp for training deep learning models.

Deeplearning4j logo Deeplearning4j

Deeplearning4j is an open-source, distributed deep-learning library written for Java and Scala.
  • NVIDIA DIGITS Landing page
    Landing page //
    2023-10-16
  • Deeplearning4j Landing page
    Landing page //
    2023-10-16

NVIDIA DIGITS features and specs

No features have been listed yet.

Deeplearning4j features and specs

  • Java Integration
    Deeplearning4j is written for Java, making it easy to integrate with existing Java applications. This is a significant advantage for businesses running Java systems.
  • Scalability
    It is designed for scalability and can be used in distributed environments. This is ideal for handling large-scale datasets and heavy computational tasks.
  • Commercial Support
    Deeplearning4j offers professional support through commercial entities, which can be beneficial for enterprises needing reliable assistance and maintenance.
  • Compatibility with Hardware
    It provides compatibility with GPUs and various processing environments, allowing efficient training of deep networks.
  • Ecosystem
    Deeplearning4j is part of a larger ecosystem, including tools like DataVec for data preprocessing and ND4J for numerical computing, providing a comprehensive suite for machine learning tasks.

Possible disadvantages of Deeplearning4j

  • Learning Curve
    It can have a steep learning curve, especially for developers not already familiar with the Java programming language or deep learning concepts.
  • Community Size
    The community and available resources are not as extensive as those for other deep learning libraries like TensorFlow or PyTorch. This might limit access to free and diverse community support.
  • Less Popularity
    Compared to more popular frameworks like TensorFlow or PyTorch, Deeplearning4j is less commonly used, which may affect library updates and third-party tool integrations.
  • Performance
    In some use cases, performance can lag behind other optimized frameworks that extensively use C++ and CUDA, particularly for specific models or complex operations.

NVIDIA DIGITS videos

+ Add

Nvidia DIGITS for Deep Learning Review and Demo

More videos:

  • Review - Building Convolutional Neural Networks with NVIDIA DIGITS

Deeplearning4j videos

+ Add

Deep Learning with DeepLearning4J and Spring Boot - Artur Garcia & Dimas Cabré @ Spring I/O 2017

Category Popularity

0-100% (relative to NVIDIA DIGITS and Deeplearning4j)

NVIDIA DIGITS

Deeplearning4j

Machine Learning
35 35%
Machine Learning
65% 65
Data Science And Machine Learning
0 0%
Data Science And Machine Learning
100% 100
AI
41 41%
AI
59% 59
Image Analysis
100 100%
Image Analysis
0% 0

User comments

Share your experience with using NVIDIA DIGITS and Deeplearning4j. For example, how are they different and which one is better?
Log in or Post with

Social recommendations and mentions

Based on our record, Deeplearning4j should be more popular than NVIDIA DIGITS. It has been mentiond 6 times since March 2021. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.

NVIDIA DIGITS mentions (2)

  • Tools for CNN training like NVIDIA DIGITS
    I'm not quite sure if this is the place to ask it, but I'll give it a shot. Several years ago, during my PhD, I used to train small CNNs using NVIDIA DIGITS tool (https://developer.nvidia.com/digits), that is basically a frontend to tasks such as build datasets, configure training parameters, follow real time training data (epochs), test classification and export training for usage. This is a oversimplified... Source: over 2 years ago
  • Does anything benefit from more than 1 GPU?
    Also frameworks which make moving to multiGPU easy, like DIGITS: https://developer.nvidia.com/digits. Source: almost 4 years ago

Deeplearning4j mentions (6)

  • DeepLearning4j Blockchain Integration: Convergence of AI, Blockchain, and Open Source Funding
    This integration is not only a technical marvel but also a case study in how open source funding and a transparent business model powered by blockchain are fostering collaboration among developers, academics, and institutional investors. With links to key resources such as the DL4J GitHub repository and the DL4J official website, the project serves as an inspiration for merging complex domains in a unified framework. - Source: dev.to / 27 days ago
  • DeepLearning4j Blockchain Integration: Merging AI and Blockchain for a Transparent Future
    DeepLearning4j Blockchain Integration is more than just a convergence of technologies; it’s a paradigm shift in how AI projects are developed, funded, and maintained. By utilizing the robust framework of DL4J, enhanced with secure blockchain features and an inclusive open source model, the project is not only pushing the boundaries for artificial intelligence but also establishing a resilient model for future... - Source: dev.to / 3 months ago
  • Machine Learning in Kotlin (Question)
    While KotlinDL seems to be a good solution by Jetbrains, I would personally stick to Java frameworks like DL4J for a better community support and likely more features. Source: almost 4 years ago
  • Does Java has similar project like this one in C#? (ml, data)
    Would recommend taking a look at dl4j: https://deeplearning4j.org. Source: about 4 years ago
  • just released my Clojure AI book
    We use DeepLearning4j in this chapter because it is written in Java and easy to use with Clojure. In a later chapter we will use the Clojure library libpython-clj to access other deep learning-based tools like the Hugging Face Transformer models for question answering systems as well as the spaCy Python library for NLP. Source: about 4 years ago
View more

What are some alternatives?

When comparing NVIDIA DIGITS and Deeplearning4j, you can also consider the following products

DeepPy - DeepPy is a MIT licensed deep learning framework that tries to add a touch of zen to deep learning as it allows for Pythonic programming.

TensorFlow - TensorFlow is an open-source machine learning framework designed and published by Google. It tracks data flow graphs over time. Nodes in the data flow graphs represent machine learning algorithms. Read more about TensorFlow.

Amazon DSSTNE - Deep Scalable Sparse Tensor Network Engine (DSSTNE) is a library for building Deep Learning (DL) and machine learning (ML) models.

PyTorch - Open source deep learning platform that provides a seamless path from research prototyping to...

Floyd - Heroku for deep learning

Keras - Keras is a minimalist, modular neural networks library, written in Python and capable of running on top of either TensorFlow or Theano.

Loading...