With a strong technical background as a backend developer, I focuse on improving my math skills. In general, the entrance threshold is quite high – you nee to know Python programming, have a good knowlege of linear algebra, numerical methods, probability theory, statistics. Machine Learning stands at the intersection of all this knowlege. Therefore, without a mathematical and technical base, “Machine Learning from scratch” courses look at least unconvincing. At the same time , developers can use certain capabilities of machine learning without delving into the specifics of working with data. There are many ready-made solutions on the market that can be adapte to business nees.
The same ones create
There are also a number of SaaS platforms (AWS, Azure, Google Cloud) that can be use to solve tasks relate to Image Processing, Speech Recognition, NLP, Translation, Video Intelligence using ready-made API services. MYTH 2 Any data is suitable for a Machine Portugal B2B List Learning algorithm. It would seem that architectures and algorithms are the most important in this process. n laboratories by specialists with PhDs in mathematics. Instead, in this area, 90% of project success is quality data. Otherwise, it is very difficult to achieve high accuracy of the model.
The mag because for him it is completely
In order to train a neural network, you nee huge data sets — they cannot be random pictures or text from the Internet. Let’s imagine that we traine our AERO Leads model on images of poor quality. When a user takes a photo of a plant on his smartphone and uploads a high-quality photo in a large magnification, the algorithm simply does not recognize different data. and reuce to a single format. The preparation and systematization of data for the model is calle markup – this is done by separate teams.
