Introduction to CNN and its practical implementation in Keras. Classification, Localization, Convolutional, Splot, Kernel, Pooling and more
One of the reasons for the expansion of the use of artificial intelligence is deep machine learning, thanks to which computers in some areas have surpassed people’s abilities. Deep learning through multiple layers of non-linear transformations distinguishes the desired features [1 ]. The Deep Blue supercomputer from IBM defeated the world chess champion Garry Kasparov, despite the fact that until recently computers were unable to solve trivial problems from a human point of view, i.e. natural language processing or recognition of objects in photographs. Both of these activities are performed outside our consciousness. In 2006 Geoffrey Hinton and other researchers presented a deep learning algorithm that recognizes handwritten figures from the MNIST database [2 ]. It contains a training set of 60,000 examples and a test set of 10,000 examples. The solution is considered to be a breakthrough because, based on the test set, the precision of the solution was over 98%.
Deep learning on graphs: successes, challenges, and next steps. TL;DR This is the first in a series of posts where I will discuss the evolution and future trends in the field of deep learning on graphs.
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PyTorch For Deep Learning — Convolutional Neural Networks ( Fashion-MNIST ). This blog post is all about how to create a model to predict fashion mnist images and shows how to implement convolutional layers in the network.
Basic fundamentals of CNN. CNN’s are a special type of ANN which accepts images as inputs. Below is the representation of a basic neuron of an ANN which takes as input X vector.
This post provides the details of the architecture of Convolutional Neural Network (CNN), functions and training of each layer, ending with a summary of the training of CNN.