Top 11 Frameworks in the AI World

Gone are the times when AI was considered to be fictional.

From robots to Google Siri and now the introduction of the new Google Duplex, Artificial intelligence seems to have taken considerable strides to become more and more humane.

The demand for machine learning and AI has grown exponentially. Additionally, the community has increased as a result, which has led to the evolution of some AI frameworks that make learning AI much easier.

In this article, we shall discuss some of the best frameworks to get you started with AI development.

Tensor Flow

Hailing from the Google family, TensorFlow proves to be a robust open-source framework that supports deep learning and which can be accessed even from a mobile device.

Tensor flow is considered an apt tool for statistical program development. As it offers distributed training, machine models can be trained much more effectively at any level of abstraction that the user prefers.

Features

A scalable multi-programming interface for easy programming
Strong growth drivers with a strong open-source community
Provides extensive and well-documented manuals for people

Pros

The language used by tensor flow is Python, which is very popular nowadays.
This framework is capable of high computational power. Hence, it can be used on any CPU or GPU.
Uses computational graph abstraction to create machine models

Cons

To make a decision or prediction, the framework passes the input data through multiple nodes. This can be time-consuming.
It also lacks many of the pre-trained models of AI.

Check out this Udemy course if you are interested in learning TensorFlow.

Microsoft CNTK

Microsoft CNTK is a faster and more versatile open-source framework based on neural networks that support text, message, and voice remodeling.

It provides an efficient scaling environment due to a faster overall evaluation of the machine models while taking care of accuracy.

Microsoft CNTK has integrations with major massive datasets, making it the leading choice to be adopted by big players like Skype, Cortana, etc., with a very expressive, easy-to-use architecture.

Features

Highly optimized to provide efficiency, scalability, speed, and high-level integrations
Has built-in components such as hyperparameter tuning, supervised learning models, reinforcement, CNN, RNN, etc.
Resources are utilized to provide the best efficiency.
Own networks that can be expressed efficiently, such as full APIs, both high-level and low level

Pros

As it supports Python and C++, this framework can work with multiple servers at once and hence makes the learning process quicker.
It has been developed keeping in mind the recent developments in the world of AI. Microsft CNTK’s architecture supports GAN, RNN, and CNN.
It permits distributed training to train machine models effectively.

Cons

It lacks a visualization board and mobile ARM support.

Caffe

Caffe is a deep learning network with a preloaded set of trained neural networks. This should be your first pick if your deadline is close.

Known for its image processing capabilities, this framework also has extended support of MATLAB.

Features

All of its models are written in plaintext schemas
It offers massive speed and highly efficient work since it is already preloaded.
An active open-source community for discussion and collaborative code.

Pros

Interlinking C, C++, and Python, it also supports the modeling of CNN(convolutional neural networks)
Efficient when computing numerical tasks due to its speed.

Cons

Caffe is not capable of handling complex data but is comparatively fast while handling the visual processing of images.

Theano

Theano is based on python, which is a proven programming language when it comes to faster processing and response.

Using GPUs in place of CPU, this framework supports deep learning research and can deliver accuracy for networks that need high computational power. For instance, the computation of multi-dimensional arrays requires high power, and Theano is capable of that.

Features

Evaluation of expressions is faster due to dynamic code generation
It provides an excellent accuracy ratio, even when values are minimal.
Unit testing is a significant feature of Theano, as it allows the user to self-verify their code as well as to detect and diagnose errors easily.

Pros

Theano offers efficient support for all data-intensive applications but requires combining with other libraries.
It is efficiently optimized for the CPU as well as the GPU.

Cons

There will be no more updates or addition of features to the current version of Theano.

Amazon machine learning

Being a trending entrant to the AI community, Amazon machine learning offers high-end support in the development of self-learning tools.

This framework already has an existing user base in its multiple services like AWS, S3, and Amazon Redshift. This is a managed service by Amazon, with three operations performed on the model: data analysis, training of the model, and evaluation.

Features

There are tailored Tools for every level of experience in the AWS, even if you are a beginner, data scientist, or developer
Security is of utmost importance, so all data is encrypted
Provides extensive tools for data analysis and comprehension
Integrations with all major datasets

Pros

You don’t need to write a lot of code with this framework. Instead, it lets you interact with the AI-powered framework via APIs.
Commonly used by data scientists, developers, and ML researchers.

Cons

It lacks flexibility as the entire framework is abstracted, so if you’d like to choose a particular normalization or machine learning algorithm, you can’t.
It also lacks data visualization.

Scikit-Learn

Scikit-Learn is a Python library built for the purpose of Machine learning. In fact, it is a collection of data mining and data analysis tools.

This AI framework is built on popular Python packages, such as NumPy, SciPy, and matplotlib. Anyone can reuse it in various contexts, including predictive data analysis.

Features

It expands on two Python libraries — NumPy and SciPy.
Distributed under the BSD license, it is free from any legal bindings.
It includes bunching, relapse, and order for AI and data mining assignments.
Use cases include spam detection, image recognition, stock pricing, etc.

Pros

It is an easy-to-use but versatile library that serves real-world purposes.
It has an international online community of authors and contributors constantly updating it.
It offers elaborate API documentation for algorithm integration with different platforms.

Cons

For those looking for an in-depth learning platform, it is not the best option.

PyTorch

PyTorch is an open-source framework for AI and ML that helps you in various tasks, ranging from research prototyping to production deployment. This platform also enables peer-to-peer communication using Python and C++.

Features

It supports frictionless scaling and development on major cloud platforms.
It has a powerful ecosystem of tools and libraries.
It lets you optimize performance and scale up distributed training.

Pros

It contains numerous modular pieces that you can combine easily.
You can write your own layer types.
It supports fast, flexible, and efficient production.

Cons

Usually, users need to write their own training code.
It does not have any commercial support.

Torch

The Torch is an open-source framework that could support numerical operations. It offers numerous algorithms for the faster development of deep learning networks.

It is used extensively in the AI labs of Facebook and Twitter. There is a python based framework known as PyTorch, which has proven to be simpler and more reliable.

Features

Features a lot of routines to index, slice, transpose with an N-dimensional array model
Optimization routines are present, primarily numeric based with neural network models
GPU support is highly efficient
Integrates easily with the iOS and Andriod

Pros

Very high flexibility regarding languages and integrations
High level of speed and GPU utilization efficiency
Pre-existing models are available to train the data on.

Cons

Documentation is not very clear to the users, so it presents a steeper learning curve
Lack of code for immediate use, so it takes time.
It is initially based on a programming language called Lua, and not many are aware of it.

Accord.Net

Accord.net is a C# based framework that assists in the development of neural networks used for audio and image processing.

Applications can use this commercially as well to produce computer vision applications, signal processing, as well as statistics applications.

Features

Mature, well-tested codebase, as it was started in 2012
Provides a comprehensive set of sample models and datasets to get your application started quickly

Pros

It is continuously supported by an active development team.
This well-documented framework efficiently handles numerical intensive computation and visualization
Implementation of algorithms and signal processing can be performed conveniently with this framework.
It can easily handle numerical optimization and artificial neural networks.

Cons

It is not very well known when compared to other frameworks.
Its performance is slower compared to other frameworks.

Apache Mahout

Apache Mahout, being an open-source framework, aims at the development of scalable machine learning frameworks. It doesn’t deal with APIs as such but helps in the implementation of new machine learning algorithms by data scientists and engineers.

Features

Known for it’s Scala DSL which is mathematically very expressive
Extends support to multiple backends that are distributed.

Pros

It aids in clustering, collaborative filtering, and classification.
Its computational operations make use of Java libraries, which are faster.

Cons

Python libraries are not as compatible as Java libraries with this framework.
Its computational operations are slower than Spark MLib.

Spark MLib

Spark MLib framework by Apache is supported by R, Scala, Java, and Python. It can be loaded with Hadoop workflows for providing machine learning algorithms such as classification, regression, and clustering.

Apart from Hadoop, it can also be integrated with the cloud, Apache, or even standalone systems.

Features

High performance is one of the key elements and is said to be 100 times faster than MapReduce
Spark is exceptionally versatile and runs in multiple computing environments

Pros

It can process vast amounts of data quickly, as it works on iterative computation.
It is available in many languages and easily pluggable.
It cycles large scales of data processing with ease.

Cons

It can be plugged with Hadoop only.
It is difficult to understand this framework’s mechanism without extensive work on the same.

AI Framework Comparision

Framework	Language	Opensource?	Features of Architecture
TensorFlow	C++ or Python	Yes	Uses data structures
Microsoft CNTK	C++	Yes	GPU/CPU based. It supports RNN, GNN, and CNN.
Caffe	C++	Yes	Its architecture supports CNN
Theano	Python	Yes	Flexible architecture allowing it to deploy in any GPU or CPU
Amazon Machine Learning	Multiple languages	Yes	Hailing from Amazon, it uses AWS.
Torch	Lua	Yes	Its architecture allows powerful computations.
Accord.Net	C#	Yes	Capable of scientific computations and pattern recognition.
Apache Mahout	Java, Scala	Yes	Capable of making machines learn without having to program
Spark MLib	R, Scala, Java, and Python	Yes	Drivers, and executors run in their processors—horizontal or vertical clusters.
Scikit-Learn	Python	Yes	Capable of data mining assignments
PyTorch	Python, C++	Yes	It supports frictionless scaling.