Top AI Tools
Artificial Intelligence Tools
Latest and Most Useful AI Tools
CNTK
CNTK allows users to easily realize and combine popular model types such as feed-forward DNNs,
convolutional nets (CNNs), and recurrent networks (RNNs/LSTMs).
It implements stochastic gradient descent (SGD, error backpropagation)
learning with automatic differentiation and parallelization across multiple GPUs and servers.
CNTK is available for anyone to try out, under an open-source license.
Auto ML
Out of all the tools and libraries listed above, Auto ML is probably one of the
strongest and a fairly recent addition to the arsenal
of tools available at the disposal of a machine learning engineer.
As described in the introduction, optimizations are of the essence in machine learning tasks.
While the benefits reaped out of them are lucrative,
success in determining optimal hyperparameters is no easy task.
This is especially true in the black box like neural networks wherein determining things that
matter becomes more and more difficult as the depth of the network increases.
H20
Open-source deep learning platform
It is an artificial intelligence tool which is business oriented and help them to make a decision
from data and enables the user to draw insights. There are two open source versions of it.
one is standard H2O and other is paid version Sparkling Water.
It can be used for predictive modelling, risk and fraud analysis, insurance analytics,
advertising technology, healthcare and customer intelligence.
OpenNN
Jumping from something that is completely beginner friendly to something meant for experienced developers, OpenNN offers an arsenal of advanced analytics.
It features a tool, Neural Designer for advanced analytics which provides graphs and tables to interpret data entries.
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Google ML Kit
Google ML Kit, Google’s machine learning beta SDK for mobile developers,
is designed to enable developers to build personalized features on Android and IOS phones.
The kit allows developers to embed machine learning technologies with app based.
APIs running on the device or in the cloud. These include features such as face.
and text recognition, barcode scanning, image labelling and more.
Developers are also able to build their own TensorFlow Lite models in cases.
where the built-in APIs may not suit the use case.
Theano
It was created to make actualizing profound learning models as
quick and simple as feasible for innovative work.
It keeps running on Python 2.7 or 3.5 and can consistently execute on GPUs and CPUs.
What sets Theano separated is that it exploits the PC’s GPU.
This enables it to make information escalated counts up to multiple times quicker
than when kept running on the CPU alone. Theano’s speed makes it particularly profitable
for profound learning and other computationally complex undertakings.
