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Artificial Intelligence has facilitated the processing of a large amount of data and its use in the industry. The number of tools and frameworks available to data scientists and developers has increased with the growth of AI and ML. This article on Artificial Intelligence Tools & Frameworks will list out some of these in the following sequence:

Development of neural networks is a long process which requires a lot of thought behind the architecture and a whole bunch of nuances which actually make up the system.

These nuances can easily end up getting overwhelming and not everything can be easily tracked. Hence, the need for such tools arises, where humans handle the major architectural decisions leaving other optimization tasks to such tools. Imagine an architecture with just 4 possible booleanhyperparameters, testing all possible combinations would take 4! Runs. Retraining the same architecture 24 times is definitely not the best use of time and energy.

Also, most of the newer algorithms contain a whole bunch of hyperparameters. Heres where new tools come into the picture. These tools not only help develop but also, optimize these networks.

From the dawn of mankind, we as a species have always been trying to make things to assist us in day to day tasks. From stone tools to modern day machinery, to tools for making the development of programs to assist us in day to day life. Some of the most important tools and frameworks are:

Scikit-learn is one of the most well-known ML libraries. It underpins many administered and unsupervised learning calculations. Precedents incorporate direct and calculated relapses, choice trees, bunching, k-implies, etc.

It includes a lot of calculations for regular AI and data mining assignments, including bunching, relapse and order. Indeed, even undertakings like changing information, feature determination and ensemble techniques can be executed in a couple of lines.

For a fledgeling in ML, Scikit-learn is a more-than-adequate instrument to work with, until you begin actualizing progressively complex calculations.

On the off chance that you are in the realm of Artificial Intelligence, you have most likely found out about, attempted or executed some type of profound learning calculation. Is it accurate to say that they are essential? Not constantly. Is it accurate to say that they are cool when done right? Truly!

The fascinating thing about Tensorflow is that when you compose a program in Python, you can arrange and keep running on either your CPU or GPU. So you dont need to compose at the C++ or CUDA level to keep running on GPUs.

It utilizes an arrangement of multi-layered hubs that enables you to rapidly set up, train, and send counterfeit neural systems with huge datasets. This is the thing that enables Google to recognize questions in photographs or comprehend verbally expressed words in its voice-acknowledgment application.

Theano is wonderfully folded over Keras, an abnormal state neural systems library, that runs nearly in parallel with the Theano library. Keras fundamental favorable position is that it is a moderate Python library for profound discovering that can keep running over Theano or TensorFlow.

What sets Theano separated is that it exploits the PCs GPU. This enables it to make information escalated counts up to multiple times quicker than when kept running on the CPU alone. Theanos speed makes it particularly profitable for profound learning and other computationally complex undertakings.

Caffe is a profound learning structure made with articulation, speed, and measured quality as a top priority. It is created by the Berkeley Vision and Learning Center (BVLC) and by network donors. Googles DeepDream depends on Caffe Framework. This structure is a BSD-authorized C++ library with Python Interface.

It allows for trading computation time for memory via forgetful backprop which can be very useful for recurrent nets on very long sequences.

If you like the Python-way of doing things, Keras is for you. It is a high-level library for neural networks, using TensorFlow or Theano as its backend.

The majority of practical problems are more like:

In all of these, Keras is a gem. Also, it offers an abstract structure which can be easily converted to other frameworks, if needed (for compatibility, performance or anything).

PyTorch is an AI system created by Facebook. Its code is accessible on GitHub and at the present time has more than 22k stars. It has been picking up a great deal of energy since 2017 and is in a relentless reception development.

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.

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.

Thus we enter a new realm of meta, wherein software helps up build software. AutoML is a library which is used by many Machine learning engineers to optimize their models.

Apart from the obvious time saved, this can also be extremely useful for someone who doesnt have a lot of experience in the field of machine learning and thus lacks the intuition or past experience to make certain hyperparameter changes by themselves.

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.

H20 is an 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.

Google ML Kit, Googles machine learning beta SDK for mobile developers, is designed to enable developers to build personalised 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.

With this, we have come to the end of our Artificial Intelligence Tools & Frameworks blog. These were some of the tools that serve as a platform for data scientists and engineers to solve real-life problems which will make the underlying architecture better and more robust.

You can check out theAI and Deep Learning with TensorFlow Course that is curated by industry professionals as per the industry requirements & demands. You will master the concepts such as SoftMax function, Autoencoder Neural Networks, Restricted Boltzmann Machine (RBM) and work with libraries like Keras & TFLearn. The course has been specially curated by industry experts with real-time case studies.

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Top 12 Artificial Intelligence Tools & Frameworks | Edureka