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How Machine Learning Models really work

In this post, I will try to explain the main idea of how to teach machine learning models to make correct decisions. For example, let’s speak about image recognition. And from the basic level of understanding, of how it works with some cats and dogs images, we can be very close to the general principle, which is used in the driverless car for the recognition of the human on the road. In the realm of technological advancements, machine learning stands as a remarkable pillar of innovation. This cutting-edge field has revolutionized the way we interact with technology, enabling computers to learn and adapt from data without being explicitly programmed. From self-driving cars to personalized recommendations, machine learning is shaping the future and opening doors to unprecedented possibilities. In this article, we delve into the essence of machine learning, exploring its key concepts, applications, and the transformative impact it has on various industries.

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So, let’s begin with the simple examples: how to teach the model to differentiate cats and dogs.
There are 2 approaches: either to choose one of ML algorithms and to begin optimization of the algorithm work. Or to use the neural network and tune its weights (means coefficients) to get the best results in the identification.

In both cases, we’ll divide the images DB into several groups: training set, validation set, and a testing set of data. The 1st group of figures will help our model to identify a cat or a dog based on the labeled samples.


The second, validation set, will help us to evaluate the accuracy of machine model study results. The last one – the testing set let us see, how the model is working on row data.
For the machine learning algorithm, the process will be consist of dividing each of the thousands of provided images into pixels. Each pixel will be coded based on its content. Similarities in pixels will be analyzed by the machine. The same procedure will be repeated on each of the images in the training data set. Results collected and compared to the know facts about the training set of cats and dogs. If the algorithm, provided by the research team, is good enough, the model shows good results. Otherwise, researchers will teach the machine further or even change the algorithm to achieve a higher rate of image recognition. How to manage the ML optimization is well described in this post.   

Of course, to find the best results would not be easy. But scientists discovered that the most effective way is not to look for the best result. This means, the highest rate of the correctly predicted answers for our training dataset, but to find the minimum of error. This called cost minimum or a minimum of loss.

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The difference in work with the neural networks included in the fact, that there is no particular algorithm or function which can be optimized. Instead, the optimization consists of turning the special coefficients, which manipulate the behavior of internal layers of the neural network. These changes can significantly influence the results of neural network operation. By turning this coefficient, we can teach the neural network to achieve such results, which are valuable for our purposes.
Testing set of data let us command the wishes to NN, while work with the validation set gives the prognosis of how well the model work. Testing set if necessary to let the researchers do the final check.

Coming back to the image recognition task with neural networks all the processes of optimization really based on tuning special parameters. It’s a complicated mathematical task, which is well described in this video: https://youtu.be/ALdsqfrLieg.
Stay with us to find out more about artificial intelligence, or sign up for the ML channel on medium.com.

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Conclusion: Pioneering the Future with Machine Learning

In a world characterized by data and driven by technology, machine learning is the compass guiding us into the unknown. Its ability to decipher complex patterns, make predictions, and automate tasks holds the promise of a more efficient, informed, and interconnected future. As we continue to harness the power of machine learning, we unlock new realms of knowledge and innovation, reshaping industries, and propelling humanity forward.

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How Machine Learning Models really work

Dec 16, 2020 | News Updates, abstract, Desktop, Science

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