In machine learning designing the structure of the model and training the neural network are relatively small elements of a longer chain of activities. We usually start with understanding business requirements, collecting and curating data, dividing it into training, validation and test subsets, and finally serving data to the model....
Preparing your data for machine learning is not a task that most AI professionals miss. Data are of different quality, most often they require very thorough analysis, sometimes manual review, and certainly selection and initial preprocessing. In the case of classification tasks, the division of a dataset into classes may...
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...
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...
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...
