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...
Do you like statistics? There is a high probability ;-) that you don't. But don't worry, statistics are not my strong point either. All in all, it's hard to say why most people shun statistics, but I feel that many statistical issues are not entirely intuitive. Just reach for the...
One of the first problems faced by AI students is how to build a development environment for machine learning. This is an ungrateful issue because there are many methods and tools available and sometimes you simply don't know which to choose and where to start. Added to this are the...
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...
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...
