Overview

Swift AI

Stacks14

Followers52

Votes0

Keras

Stacks1.1K

Followers1.1K

Votes22

Keras vs Swift AI: What are the differences?

Keras vs Swift AI: Key Differences

Keras and Swift AI are two popular frameworks for developing artificial intelligence (AI) models. While both frameworks serve the purpose of creating AI models, there are several key differences between them.

API Design: Keras, being a high-level API, focuses on simplicity and ease of use. It provides a user-friendly interface, making it accessible to beginners and researchers. On the other hand, Swift AI, which is built using Apple's Swift programming language, offers a low-level API with more control and flexibility for advanced users and developers.
Supported Platforms: Keras is a framework that can run on top of different backend engines such as TensorFlow, Theano, and CNTK, providing cross-platform compatibility. Swift AI, on the other hand, is specifically designed for Apple's platforms, including iOS, macOS, tvOS, and watchOS.
Community Support: Keras has a large and active community of developers and researchers, making it easier to find resources, tutorials, and community-driven contributions. Swift AI, being a relatively newer framework, has a smaller community but is quickly growing with the popularity of the Swift programming language.
Integration with Other Tools: Keras provides seamless integration with popular Python libraries like NumPy and Pandas, which are widely used in data preprocessing and analysis. Swift AI, being built on Swift, can leverage the power of SwiftUI for building user interfaces and integrates well with other Apple frameworks and tools.
Model Deployment: Keras offers a wide range of deployment options, including serving models with RESTful APIs or deploying them on edge devices like Raspberry Pi. Swift AI, being tailored for Apple's platforms, excels in deploying AI models on iOS devices, taking advantage of Apple's Core ML framework and hardware optimization.
Learning Curve: Due to its high-level abstraction and simplicity, Keras is considered to have a relatively lower learning curve, making it easier for beginners to get started with AI model development. Swift AI, being more low-level, requires a deeper understanding of the Swift programming language and the underlying concepts of AI, making it more suitable for experienced developers.

In summary, Keras and Swift AI differ in terms of API design, supported platforms, community support, integration with other tools, model deployment options, and learning curve.

Advice on Swift AI, Keras

Adithya

Student at PES UNIVERSITY

May 11, 2020

Needs advice

I have just started learning some basic machine learning concepts. So which of the following frameworks is better to use: Keras / TensorFlow/PyTorch. I have prior knowledge in python(and even pandas), java, js and C. It would be nice if something could point out the advantages of one over the other especially in terms of resources, documentation and flexibility. Also, could someone tell me where to find the right resources or tutorials for the above frameworks? Thanks in advance, hope you are doing well!!

107k views107k

Comments

philippe

Research & Technology & Innovation | Software & Data & Cloud | Professor in Computer Science

Sep 13, 2020

Review

Hello Amina, You need first to clearly identify the input data type (e.g. temporal data or not? seasonality or not?) and the analysis type (e.g., time series?, categories?, etc.). If you can answer these questions, that would be easier to help you identify the right tools (or Python libraries). If time series and Python, you have choice between Pendas/Statsmodels/Serima(x) (if seasonality) or deep learning techniques with Keras.

Good work, Philippe

4.63k views4.63k

Comments

Fabian

Software Developer at DCSIL

Feb 11, 2021

Decided

For my company, we may need to classify image data. Keras provides a high-level Machine Learning framework to achieve this. Specifically, CNN models can be compactly created with little code. Furthermore, already well-proven classifiers are available in Keras, which could be used as Transfer Learning for our use case.

We chose Keras over PyTorch, another Machine Learning framework, as our preliminary research showed that Keras is more compatible with .js. You can also convert a PyTorch model into TensorFlow.js, but it seems that Keras needs to be a middle step in between, which makes Keras a better choice.

55.4k views55.4k

Comments

Detailed Comparison

Swift AI	Keras
Swift AI is a high-performance AI and machine learning library written entirely in Swift. We currently support iOS and OS X, with support for more platforms coming soon!	Deep Learning library for Python. Convnets, recurrent neural networks, and more. Runs on TensorFlow or Theano. https://keras.io/
Feed-Forward Neural Network; Fast Matrix Library	neural networks API;Allows for easy and fast prototyping;Convolutional networks support;Recurent networks support;Runs on GPU
Statistics
Stacks 14	Stacks 1.1K
Followers 52	Followers 1.1K
Votes 0	Votes 22
Pros & Cons
No community feedback yet	Pros 8 Quality Documentation 7 Supports Tensorflow and Theano backends 7 Easy and fast NN prototyping Cons 4 Hard to debug
Integrations
Swift	TensorFlow scikit-learn Python

What are some alternatives to Swift AI, Keras?

TensorFlow

TensorFlow is an open source software library for numerical computation using data flow graphs. Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) communicated between them. The flexible architecture allows you to deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device with a single API.

scikit-learn

scikit-learn is a Python module for machine learning built on top of SciPy and distributed under the 3-Clause BSD license.

PyTorch

PyTorch is not a Python binding into a monolothic C++ framework. It is built to be deeply integrated into Python. You can use it naturally like you would use numpy / scipy / scikit-learn etc.

Kubeflow

The Kubeflow project is dedicated to making Machine Learning on Kubernetes easy, portable and scalable by providing a straightforward way for spinning up best of breed OSS solutions.

TensorFlow.js

Use flexible and intuitive APIs to build and train models from scratch using the low-level JavaScript linear algebra library or the high-level layers API

Polyaxon

An enterprise-grade open source platform for building, training, and monitoring large scale deep learning applications.

Streamlit

It is the app framework specifically for Machine Learning and Data Science teams. You can rapidly build the tools you need. Build apps in a dozen lines of Python with a simple API.

MLflow

MLflow is an open source platform for managing the end-to-end machine learning lifecycle.

H2O

H2O.ai is the maker behind H2O, the leading open source machine learning platform for smarter applications and data products. H2O operationalizes data science by developing and deploying algorithms and models for R, Python and the Sparkling Water API for Spark.

PredictionIO

PredictionIO is an open source machine learning server for software developers to create predictive features, such as personalization, recommendation and content discovery.