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...
If you are a Python programmer and use the AWS and Anaconda environments, sooner or later you will come across the need to run a Python script as a cron process on Amazon Linux in the Anaconda environment. This shouldn't be difficult, right? Hmmm, unfortunately it is. Because I spent...
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...
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...
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...
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...
