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11 Alternatives To Keras For Deep Learning Enthusiasts

11 Alternatives To Keras For Deep Learning Enthusiasts


Keras, one of the most popular frameworks in deep learning, is a high-level neural network library which runs on top of TensorFlow, CNTK and Theano. Written in Python, this framework allows for easy and fast prototyping as well as running seamlessly on CPU as well as GPU.  

In this article, we are listing down the top 11 alternatives to Keras, the popular deep learning library:



(The list is in alphabetical order)

1| CUDA

CUDA (Compute Unified Device Architecture) is a parallel computing platform and application programming interface (API) model created by NVIDIA. It includes GPU-accelerated libraries, debugging and optimisation tools, a C/C++ compiler and a runtime library to deploy your application. With the help of the CUDA Toolkit, one can develop, optimise and deploy applications on GPU-accelerated embedded systems, desktop workstations, enterprise data centres, cloud-based platforms, and HPC supercomputers.

2| Deeplearning4j

Deeplearning4j is an open-sourced deep learning programming library which is written for Java and is compatible with any JVM language, such as Scala, Clojure or Kotlin. The underlying computations of this library are written in C, C++, and Cuda

3| DeepPy

DeepPy is an MIT-licensed deep learning framework for designing models with complex architectures. Techniques like LSTM and Batch Normalisation are implemented inside this framework and it maintains a clean high-level interface. This framework allows for Pythonic programming based on NumPy, runs on CPU or Nvidia GPUs, implements various network architectures like Feedforward, Covnets, Autoencoders, among others. 

4| Infer.NET

Infer.NET is a machine learning framework for running Bayesian inference in graphical models. It provides state-of-the-art message-passing algorithms and statistical routines needed to perform inference for a wide variety of applications. There are various intuitive features in this framework such as rich modelling language, multiple inference algorithms, designed for large scale inference as well as user-extendable. With the help of this framework, various Bayesian models such as Bayes Point Machine classifiers, TrueSkill matchmaking, hidden Markov models, and Bayesian networks can be implemented with ease.

5| ML Kit

ML Kit is a mobile software development kit which provides convenient APIs that help to use custom TensorFlow Lite models in mobile apps. The ready to use APIs for common mobile use cases include recognizing text, detecting faces, identifying landmarks, scanning barcodes, labelling images, and identifying the language of the text. With just a few lines of codes, this framework can enable cloud-based processing, the real-time capabilities of mobile-optimised on-device models and much more.

6| NLTK

Natural Language Toolkit (NLTK) is a platform for building Python programs to work with human language data. This toolkit is one of the most powerful NLP libraries which contains packages for machine learning. It provides easy-to-use interfaces along with a suite of text processing libraries for classification, tokenization, stemming, tagging, parsing, and semantic reasoning. 

7| PredictionIO

PredictionIO is an open-source machine learning server which is built on top of state-of-the-art open source stack, Spark, MLlib, HDFS, and Elasticsearch. This framework includes a number of useful features such as it can respond to dynamic queries in real-time, choose from a wide variety of templates implementing important machine learning algorithms, deploy multiple engines to support multiple application features, efficiently use Huge Data or small data with flexible scaling and much more.  

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8| ScikitLearn 

One of the most popular libraries of machine learning, ScikitLearn is a Python module for machine learning which is built on top of SciPy, NumPy and Matplotlib. The library features a number of classification, regression and clustering algorithms such as Support Vector Machines (SVM), Random Forest, Gradient Boosting, k-means clustering, among others.

9| TensorFlow

Originally developed by the researchers at Google Brain, TensorFlow is one of the popular machine learning libraries. Often times, it is being compared with Keras. This is an end-to-end open-source platform for machine learning which has a comprehensive, flexible ecosystem of tools, libraries, and community resources in order to build and deploy machine learning applications. 

10| Theano

Theano is a popular Python library which allows a developer to define, optimise, and evaluate mathematical expressions involving multi-dimensional arrays efficiently. There are various intuitive features in this library such as tight integration with NymPy, transparent use of a GPU, efficient symbolic differentiation, speed and stability optimisations, extensive unit-testing and self-verification, among others. 

11| Torch

Torch is an opensource machine learning library which supports a wide range of machine learning algorithms that puts GPUs first. This scientific computing framework includes an easy and fast scripting language known as LuaJIT, and an underlying C/CUDA implementation. Torch supports linear algebra routines, numeric optimization routines, neural network, energy-based models and much more.


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