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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:
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:
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
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:
Website - https://www.tensorflow.org/
Github - https://github.com/tensorflow/
2. Scikit-learn
Website - https://scikit-learn.org/stable/
Github - https://github.com/scikit-learn/scikit-learn
3. Caffe
Website - https://caffe.berkeleyvision.org/
Github - https://github.com/BVLC/caffe
4. Theano
Website - http://deeplearning.net/software/theano/
Github - https://github.com/Theano/Theano
5. Keras
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.
Website - https://keras.io/
Github - https://github.com/keras-team/keras/
6. MXNET
Website - https://mxnet.apache.org/
Github - https://github.com/apache/incubator-mxnet
7. Microsoft CNTK
Website - https://docs.microsoft.com/en-us/cognitive-toolkit/
Github - https://github.com/microsoft/CNTK
8. Auto ML
Website - https://cloud.google.com/automl
Github - https://github.com/windmaple/awesome-AutoML
9. Torch/pyTorch
Website - https://pytorch.org/
Github - https://github.com/pytorch/pytorch
10. Accord.NET
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.
Already new and use all these tools? You should try to start your career in AI and you can do it here!
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We will send mail once in a week about latest updates on open source tools and technologies. subscribe our newsletterAs enterprises struggle to save and analyze more and more data, data quality suffers. Real world data contains multiple records belonging to the same customer. These records can be in single or multiple systems and they have variations across fields which makes it hard to combine them together, especially with growing data volumes. This hurts customer analytics and no AI algorithm for segmentation can produce right results.
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I have been writing on the desktop image processing application. At the version 1.0, I use the octree color quantization algorithm to reduce image to 256 colors, which is highly memory efficient with each pixel assigned the color at the center of the octree bin in which it falls. On the other hand, generates the palette using the distribution of colors in the image, but it does not consider the frequency of color. This means that if an image is composed of similar colors overall but has many different low-frequency colors or noise, octree's results can be very poor.
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