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Showing posts from September, 2019

The Limits of Artificial Intelligence

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Photo by Matan Segev from Pexels If you are here, it means that you are familiar with term artificial intelligence. Either you have read about it in school or have seen it in sci-fi movies or somewhere else. Talking about the limitations of AI, let me ask you one simple question first, do you know the definition of AI? You might be thinking to answer me with a yes, yes I know what is artificial intelligence. But what if I tell you that AI is a buzzword and it is almost impossible to properly define. It is this way because the definition of artificial intelligence is moving. People don’t call the things AI that they used to call. For example, a problem that seemed too complex to be solved by human and was solved by AI algorithm is no longer a problem of AI. Playing chess, is one of the examples. It was considered the peek level of artificial intelligence back in previous century. Now it hardly fits the criteria for AI. It is presented to the world as a super power that when

Build Your First Nueral Network: Basic Image Classification Using Keras

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Image classification is one of the most important problem to solve in machine learning. It can provide vital solutions to a variety of computer vision problems, such as face recognition, character recognition, object avoidance in autonomous vehicles and many others. Convolutional Neural Network (CNN), since its inception has been used for image classification and other computer vision problems. It is called convolutional neural network because of convolutional layer. Keras is a high level library which provides an easy way to get started with machine learning and neural networks. It will be used here to implement CNN to classify handwritten digits of MNIST dataset. Image Classification is  a process to determine which of the given classes an input image belongs to. CNNs represent a huge breakthrough in image classification. In most cases, CNN outperforms other image classification methods and provides near to human-level accuracy. CNN models do not simply spit the class nam