k-nearest neighbors for handwriting recognition

If I had to indicate one algorithm in machine learning that is both very simple and highly effective, then my choice would be the k-nearest neighbors (KNN). What’s more, it’s not only simple and efficient, but it works well in surprisingly many areas of application. In this post I decided to check its effectiveness in …

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Convolutional neural network 4: data augmentation

In the previous three parts of the tutorial, we learned about convolutional networks in detail. We looked at the convolution operation, the convolutional network architecture, and the problem of overfitting. In the classification of the CIFAR-10 dataset we achieved 81% on the test set. To go further we would have to change the architecture of …

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Convolutional neural network 3: convnets and overfitting

Convolutional neural network is one of the most effective neural network architecture in the field of image classification. In the first part of the tutorial, we discussed the convolution operation and built a simple densely connected neural network, which we used to classify CIFAR-10 dataset, achieving accuracy of 47%. In the second part of the …

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Convolutional neural network 2: architecture

Convolutional neural network provides one of the best classification results for images. In the previous post, you had the opportunity to learn what a convolution is and how to classify a CIFAR-10 dataset using a simple densly connected neural network. By the way, we have obtained accuracy of 47% on the test set. In the …

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Convolutional neural network

Convolutional neural network 1: convolutions

Deep neural networks are widely used in image and shape recognition. Examples of applications include face recognition, image analysis in medicine, handwriting classification, and detection of surrounding objects. A special type of neural network that handles image processing extremely well is a convolutional neural network. I have to admit that ConvNet is my favorite deep …

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Logistic Regression for binary classification

Logistic regression and Keras for classification

Today I would like to present an example of using logistic regression and Keras for the binary classification. I know that this previous sentence does not sound very encouraging 😉 , so maybe let’s start from the basics. We divide machine learning into supervised and unsupervised (and reinforced learning, but let’s skip this now). Supervised …

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Handwriting digit recognition Keras MNIST

Handwritten Digit Recognition with Keras

Shape recognition, and handwritten digit recognition in particular, is one of the most graceful topics for anyone starting to learn AI. There are several reasons, but the two most important are the ease with which we can use well-prepared ready-made datasets and the ability to visualize these data. From this tutorial you will learn: Okay, …

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