Top 10 AI development tools which you should know in 2020

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It is a fact the 2020 is not going the way we expected to be but when it comes to technology breakthrough we can say 2020 will be the heir of greatness.
Speaking of technical breakthroughs we have got artificial intelligence which is known to be taking over the mankind like a wildfire. Everything around us is connected through AI be it shopping travelling or even reading. Every other activity of ours is transforming into a whole new extent.

Though why is a dominated technology most of the people out there have no idea what exactly is AI. So if you are amongst them then this article is just for you as we have mentioned the exact definition of artificial intelligence and 10 of its greatest framework tools which you as a developer should know in 2020.

What Exactly is Artificial Intelligence?

As per quite a few technical experts artificial intelligence is referred to a digital computer system that demonstrate behaviour which are associated with human intelligence like problem-solving, manipulation, motion, knowledge representation, and reasoning.

Moreover scientists and developers are working on making robots that understand our emotions and respond to us accordingly and logically.
Whether you believe it or not but artificial intelligence is everywhere. Bots like Siri, Alexa, Google Assistant, and Bixby are perfect examples of artificial intelligence which are using this technologies to make our lives better.

Following is the list of areas which AI is known to have conquered:

  • Handwriting recognition
  • Speech recognition
  • Face recognition
  • Photo and video manipulation
  • Optical character recognition
  • Artificial creativity
  • Computer vision, virtual reality, and image processing

This this isn’t the only list we have curated in this article, following is the list of examples where AI is affecting our daily lives:

  • Email categorisation
  • Voice to text features
  • Assistants like Siri Cortana and bixby
  • Automated responders and online customer support
  • Process automation
  • Security surveillance
  • Sales and business forecasting

 

If you want to work on AI, then you don't need to build from scratch. You can choose any of the below frameworks and start building your application using the framework.

1. TensorFlow

tensorflow

When it comes down to popularity, there is no doubt about the fact that TensorFlow is ranked as the most known framework among all others. What makes this framework stand out is the fact that it offers high performance numerical computations along with excellent architecture support. It is also known to be beneficial for hassle-free deployment of computations across various devices. Here are some benefits of this tool:

  • Offers robots production
  • Hassle-free model building
  • Supports powerful experiments
  • Offers distributed training

Website - https://www.tensorflow.org/

Github   - https://github.com/tensorflow/


2. Scikit-learn

scikit-learn

It is one of those top tools for AI development that use python to provide learning algorithms. That is also the reason why Scikit-learn is considered to be one of the most powerful open source development tools. As of now it is is being spread out under different Linux distributions. What makes developers choose Scikit-learn is the fact that it encourages commercial and academic use. Following are some features of this amazing AI tool:

  • It offers clustering
  • Has cross-validation
  • Offers feature selection and extraction
  • It is a supervised model

Website - https://scikit-learn.org/stable/

Github - https://github.com/scikit-learn/scikit-learn

 

3. Caffe

caffe

Caffe is yet another popular framework in the sphere of artificial intelligence. Having the capability of speed expression and modularity, Caffe is known for supporting developers and researches in the best possible way. Caffe is an acronym for Convolutional Architecture for Fast Feature Embedding. Here we present you some features of Caffe:

  • Active development
  • Speed
  • Expressive architecture
  • Huge community of developers

Website - https://caffe.berkeleyvision.org/

Github - https://github.com/BVLC/caffe

4. Theano

theano

When it comes down to defining optimising and evaluating mathematical expressions then Theano is the way to go. Those who don't know, let
us tell you that, Theano is a python library that support expressions with multidimensional arrays. Ever since its inception in 2007, it has been empowering scientific investigations which are used in numbers and computations. the best part is, Theano can combine computer algebra systems with the aspects of the compiler. Here are the features of Theano:

  • Transparent usage of GPU
  • Dynamic generation of C code
  • Integration with NumPy
  • Symbolic differentiation

Website - http://deeplearning.net/software/theano/

Github - https://github.com/Theano/Theano


5. Keras

keras

What made us put Keras in this list is its ability e to run on top of Theano and TensorFlow. It is is considered to be a neutral networks API which is also higher in level when compared to its competitors. What makes developers use keras is that it offers faster experimentation.

The best part about Keras is that it can run hassle free on both CPU and GPU. Moreover, it supports both network types; convolutional and recurrent.

  • Ease of extensibility
  • User- friendliness
  • Modularity
  • Fewer cognitive loads
  • Python integration

Website - https://keras.io/

Github - https://github.com/keras-team/keras/

 

6. MXNET

Apache MXNET

MXNET is yet another top open source deep learning framework. This framework in particular is popular amongst industries like transportation healthcare and manufacturing as it is quite powerful. Moreover, this framework is flexible Ultra scalable and lean. Following are some of its features:

  • Multiple language bindings
  • Hybrid front end
  • Distributed training
  • Scalability
  • TVM support

Website - https://mxnet.apache.org/

Github - https://github.com/apache/incubator-mxnet

 

7. Microsoft CNTK

microsoft-cntk

Being a deep learning library release by Microsoft, CNTK uses a directed graph to describe neutral frameworks as the steps of computational series. CNTK is an acronym for Computational Network ToolKit. Irrespective of the location of GPUs; this framework allows parallelisation across various GPUs used and multiple machines. Here are the features of Microsoft CNTK:

  • Performance
  • Efficiency
  • Flexibility

Website - https://docs.microsoft.com/en-us/cognitive-toolkit/

Github - https://github.com/microsoft/CNTK


8. Auto ML

Google AutoML

Auto ML is a learning model framework which was launched by Google in May 2017. The only reason it was launched was to support the neutral network architecture search of Google. With that being said, AutoML is said to be the most powerful and flexible AI frameworks. Auto ML is not open source but there are lot of open source and commercial projects were using Auto ML.

  • Quick and hassle free deployment
  • Smooth train machine learning models
  • Best-in-class performance

Website - https://cloud.google.com/automl

Github - https://github.com/windmaple/awesome-AutoML

9. Torch/pyTorch

pytorch

Torch is known to be a scientific computing framework and a scripting language altogether. Framework is solely dependent upon Lua programming language. It has the capability to serve the entire range of algorithms just for deep machine learning. With LuaJT by its side, torch said to be the most reliable machine learning and AI framework tool. Following are some of its features:

  • Multiple routines for slicing indexing and transposing
  • Numerix optimisation routines
  • Linear algebra routines
  • Use ecosystem for community-driven package

Website - https://pytorch.org/

Github - https://github.com/pytorch/pytorch


10. Accord.NET

Accord NET

As the name suggest it is a .NET best machine learning framework that is preferred for commercial use as it has the ability e of developing production grade computer vision along with statistics applications and signal processing. Here we present you some of the features of Accord.NET:

  • Classification
  • Clustering
  • Hypothesis tests
  • Transformation of audio signals

Website - http://accord-framework.net/

Github - https://github.com/accord-net/framework

In case if your planning on implementing AI into your business or an application, then we highly recommend you to work with mobile application development company as it can offer you the best possible results.

 


   

Anshul Sharma is the CEO of a leading Android app development company , but apart from being one, he is deep into motivating others to achieve the best in their lives as he is a motivational speaker and quite a good one indeed

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