Take your machine learning to the next level with these artificial intelligence technologies.
Artificial
intelligence (AI) technologies are quickly transforming almost every sphere of
our lives. From how we communicate to the means we use for transportation, we
seem to be getting increasingly addicted to them.
Because
of these rapid advancements, massive amounts of talent and resources are
dedicated to accelerating the growth of the technologies.
Here
is a list of 8 best open source AI technologies you can use to take your
machine learning projects to the next level.
1. TensorFlow
Initially
released in 2015, TensorFlow is an open source machine
learning framework that is easy to use and deploy across a variety of
platforms. It is one of the most well-maintained and extensively used
frameworks for machine learning.
Created
by Google for supporting its research and production objectives, TensorFlow is
now widely used by several companies, including Dropbox, eBay, Intel, Twitter,
and Uber.
TensorFlow
is available in Python, C++, Haskell, Java, Go, Rust, and most recently,
JavaScript. You can also find third-party packages for other programming
languages.
The
framework allows you to develop neural networks (and even other computational
models) using flowgraphs.
2. Keras
Initially
released in 2015, Keras is an open source software library
designed to simplify the creation of deep learning models. It is written in
Python and can be deployed on top of other AI technologies such as TensorFlow,
Microsoft Cognitive Toolkit (CNTK), and Theano.
Keras
is known for its user-friendliness, modularity, and ease of extensibility. It
is suitable if you need a machine learning library that allows for easy and
fast prototyping, supports both convolutional and recurrent networks, and runs
optimally on both CPUs (central processing units) and GPUs (graphics processing
units).
3. Scikit-learn
Initially
released in 2007, scikit-learn is an open source
library developed for machine learning. This traditional framework is written
in Python and features several machine learning models including
classification, regression, clustering, and dimensionality reduction.
Scikit-learn
is designed on three other open source projects—Matplotlib, NumPy, and
SciPy—and it focuses on data mining and data analysis.
4. Microsoft Cognitive Toolkit
Initially
released in 2016, the Microsoft Cognitive Toolkit (previously
referred to as CNTK), is an AI solution that can empower you to take your
machine learning projects to the next level.
Microsoft
says that the open source framework is capable of "training deep learning
algorithms to function like the human brain."
Some
of the vital features of the Microsoft Cognitive Toolkit include highly
optimized components capable of handling data from Python, C++, or BrainScript,
ability to provide efficient resource usage, ease of integration with Microsoft
Azure, and interoperation with NumPy.
5. Theano
Initially
released in 2007, Theano is an open
source Python library that allows you to easily fashion various machine
learning models. Since it's one of the oldest libraries, it is regarded as an
industry standard that has inspired developments in deep learning.
At
its core, it enables you to simplify the process of defining, optimizing, and
assessing mathematical expressions.
Theano
is capable of taking your structures and transforming them into very efficient
code that integrates with NumPy, efficient native libraries such as BLAS,
and native code (C++).
Furthermore,
it is optimized for GPUs, provides efficient symbolic differentiation, and
comes with extensive code-testing capabilities.
6. Caffe
Initially
released in 2017, Caffe (Convolutional
Architecture for Fast Feature Embedding) is a machine learning framework that
focuses on expressiveness, speed, and modularity. The open source framework is
written in C++ and comes with a Python interface.
Caffe's
main features include an expressive architecture that inspires innovation,
extensive code that facilitates active development, fast performance that
accelerates industry deployment, and a vibrant community that stimulates
growth.
7. Torch
Initially
released in 2002, Torch is a machine learning library that
offers a wide array of algorithms for deep learning. The open source framework
provides you with optimized flexibility and speed when handling machine
learning projects—without causing unnecessary complexities in the process.
It
is written using the scripting language Lua and comes with an underlying C
implementation. Some of Torch's key features include N-dimensional arrays,
linear algebra routines, numeric optimization routines, efficient GPU support,
and support for iOS and Android platforms.
8. Accord.NET
Initially
released in 2010, Accord.NET is a machine learning
framework entirely written in C#.
The
open source framework is suitable for production-grade scientific computing.
With its extensive range of libraries, you can build various applications in
artificial neural networks, statistical data processing, image processing, and
many others.
Conclusion
Before
starting to build a machine learning application, selecting one technology from
the many options out there can be a difficult task. Therefore, it's important
to evaluate several options before making a final decision.
Furthermore,
learning how the various machine learning technologies work
can assist you to make a good decision.
Apart
from the above-listed AI technologies in machine learning, which are you using
in your projects? Is there any other framework, library, or toolkit not
discussed? Please let us know in the comment section below.
0 comments:
Post a Comment