Abstract
The article consists analyzes the transition of the scientific community's attention from the main tool for pat- tern recognition using machine learning methods without using neural networks to convolutional neural networks as the best way to classify objects in images. The concepts of a neural network and a convolutional neural network are consid- ered in detail, examples of one-layer, two-layer neural networks are given, a principle is shown that can be used to cre- ate a neural network of any depth, the distinctive features of convolutional neural networks has been analyzed, the types of layers that a convolutional neural network can have are analyzed. The process of training a neural network on the Fashion MNIST dataset using the TensorFlow software package from Google has been analyzed, the structure of its own convolutional neural network has been selected, the neural network has been trained to recognize various types of clothing. The result of the model's work on the Fashion MNIST test dataset was 90%. Analysis of the Tensor-Flow package showed the simplicity of the high-level API provided by the module, which allows you to build, configure and train neural networks of any complexity, including convolutional neural networks, which makes TensorFlow easy to integrate and use in your own development.
Keywords
neural network, convolutional neural network, GPU, CPU, neuron, activation function, TensorFlow, loss function, quality metric.
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