Fundamentals of Working with TensorFlow: Syntax and Basic Operations

The article is devoted to the fundamentals of working with the TensorFlow library, one of the most popular and functional platforms for creating and training machine learning and deep learning models. The paper provides a detailed examination of the key concepts underlying the computational architecture of TensorFlow, including the principles of forming and processing tensors, their properties, data types, dimensions, and ways of representing them within the computational graph. Special attention is given to basic tensor operations such as arithmetic computations, aggregation functions, matrix multiplication, and the broadcasting mechanism, which plays an important role in optimizing computations and building models. The article further explains the differences between variables and constants in TensorFlow, their roles in the process of training neural networks, and the methods used to modify or update tensor values. Emphasis is placed on how TensorFlow’s data structures enable efficient automatic differentiation and optimization of model parameters. The material is accompanied by practical exercises and code examples designed for beginning researchers and students learning machine learning technologies. The presented examples demonstrate the creation of simple computational models, the use of core TensorFlow operations, and the connection between theoretical principles and their programmatic implementation. The article can serve as an introductory guide for those taking their first steps in studying TensorFlow and seeking to develop confident practical skills with this library.