What is Keras?

Keras is designed primarily for human users, focusing on usability rather than machine efficiency. It follows best practices to minimize cognitive load by offering consistent and intuitive APIs that cut down on the number of required steps for common tasks while providing clear and actionable error messages. It also features extensive documentation and developer resources to assist users. Notably, Keras is the most popular deep learning framework among the top five teams on Kaggle, highlighting its widespread adoption and effectiveness. By streamlining the experimentation process, Keras empowers users to implement innovative concepts much faster than their rivals, which is key for achieving success in competitive environments. Built on TensorFlow 2.0, it is a powerful framework that effortlessly scales across large GPU clusters or TPU pods. Making full use of TensorFlow's deployment capabilities is not only possible but also remarkably easy. Users can export Keras models for execution in JavaScript within web browsers, convert them to TF Lite for mobile and embedded platforms, and serve them through a web API with seamless integration. This adaptability establishes Keras as an essential asset for developers aiming to enhance their machine learning projects effectively and efficiently. Furthermore, its user-centric design fosters an environment where even those with limited experience can engage with deep learning technologies confidently.

Integrations

Offers API?:
Yes, Keras provides an API

Screenshots and Video

Keras Screenshot 1

Company Facts

Company Name:
Keras
Company Location:
United States
Company Website:
keras.io

Product Details

Deployment
SaaS
Windows
Mac
Linux
iPhone
iPad
Android
Training Options
Documentation Hub
Support
Web-Based Support

Product Details

Target Company Sizes
Individual
1-10
11-50
51-200
201-500
501-1000
1001-5000
5001-10000
10001+
Target Organization Types
Mid Size Business
Small Business
Enterprise
Freelance
Nonprofit
Government
Startup
Supported Languages
English

Keras Categories and Features

Deep Learning Software

Convolutional Neural Networks
Document Classification
Image Segmentation
ML Algorithm Library
Model Training
Neural Network Modeling
Self-Learning
Visualization

Keras Customer Reviews

Write a Review
  • Reviewer Name: A Verified Reviewer
    Position: Principal Software Engineer
    Has used product for: Less than 6 months
    Uses the product: Yearly
    Org Size (# of Employees): 100 - 499
    Feature Set
    Layout
    Ease Of Use
    Cost
    Customer Service
    Would you Recommend to Others?
    1 2 3 4 5 6 7 8 9 10

    Interface for neural networks

    Date: Aug 03 2022
    Summary

    Keras is an incredibly powerful tool that makes it much easier to create deep learning models. It's free and is used by many scientific research organizations worldwide.

    Positive

    - acts as a human readable API for neural networks built on TensorFlow
    - makes reading and writing machine learning code much easier
    - can scale to large GPU clusters
    - models can be run in JavaScript, on any device
    - used by CERN and NASA in their scientific experiments
    - great documentation

    Negative

    - still requires a background in data science and ML to be able to write code

    Read More...
  • Reviewer Name: A Verified Reviewer
    Position: Researcher
    Has used product for: Less than 6 months
    Uses the product: Weekly
    Org Size (# of Employees): 26 - 99
    Would you Recommend to Others?
    1 2 3 4 5 6 7 8 9 10

    Easy to use for Python developers

    Date: Aug 08 2020
    Summary

    If you don't have too much time to really get the hang of using Tensorflow and are already a decent Python developer, Keras is a pretty good option to examine. It's a wrapper for Tensorflow so you get most of the benefits.

    Positive

    I mostly code in Python, so using Keras for my deep learning needs wasn't too hard to get used to, given the abundance of documentation and ease of writing modular code with its API.

    Negative

    Keras only has high level APIs, unlike Tensorflow, which has both high and low level support.

    Read More...
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