KerasKeras simplifies deep neural network development with its intuitive interface, making it a go-to for researchers and developers.

Keras is a high-level Python API for neural networks, designed for rapid prototyping and ease of use, capable of running on TensorFlow, CNTK, or Theano.

Keras is a high-level neural networks API, written in Python and capable of running on top of TensorFlow, CNTK, or Theano. It enables fast experimentation with deep neural networks. Designed for ease of use and rapid prototyping.

Not yet. This listing is compiled from the vendor’s public documentation, pricing pages and changelogs — nothing on this page is presented as a hands-on test result.Keras sits in our testing queue; when we run it, this section will state what we tested, how long for, and what it actually produced. How we review AI tools.

• Image Classification and Recognition: Building models to identify objects and patterns within images, widely used in computer vision.
• Natural Language Processing (NLP): Developing applications for text analysis, sentiment detection, machine translation, and chatbots.
• Time Series Analysis: Forecasting future trends or identifying patterns in sequential data, such as stock prices or sensor readings.
• Reinforcement Learning: Creating agents that learn to make decisions in an environment through trial and error.
• Speech Recognition: Developing systems that convert spoken language into text.

• Sequential and functional model APIs
• Pre-trained models for transfer learning
• Built-in layers, optimizers, loss functions, and metrics
• Data preprocessing and augmentation utilities
• Easy model saving and loading
• Distributed training capabilities
• Support for custom layers and models

Prices as of July 25, 2026, taken from Keras’s public pricing page. Vendors change pricing without notice — check before you buy.

• Abstraction can sometimes hinder deep customization.
• Requires a backend framework to function.

• PyTorch
• TensorFlow
• Scikit-learn

1. Install Keras and a compatible backend like TensorFlow using pip.
2. Import necessary modules and define your neural network model using Keras layers.
3. Compile your model with an optimizer and loss function, then train it on your data.

Use a SynaBot assistant to produce the thinking, then move the output into Keras for execution. Every SynaBot assistant is free to try on the Lite plan.

• Content Creator (ZARA) — drafts the copy, captions and campaign angles you'll run through Keras.
• Business Planner (VIKRAM) — decides whether Keras belongs in your stack and what it should replace.
• Project Manager (PACE) — turns the rollout of Keras into owned, dated tasks.

Browse the full AI assistant roster, grab a starting point from the prompt library, or have us wire it together with our AI consultancy service.