Bulk and local magnetic susceptibility of ErB12

High precision measurements of magnetoresistance Δρ/ρ = f(T,H) and magnetization M(T,H) were carried out on single crystals of rare-earth dodecaboride $ErB_{12}$ at temperatures in the interval 1.8-30 K in magnetic fields up to 70 kOe. The high accuracy of the experiments allowed us to perform numerical differentiation and analyze quantitatively the behavior of the derivative d(Δρ/ρ)/dH = f(T,H) and of the magnetic susceptibility χ(T,H) = dM/dH in paramagnetic and magnetically ordered (antiferromagnetic, $T_N$ ≈ 6.7 K and $T_M$ ≈ 5.85 K) phases of $ErB_{12}$. It was shown that negative magnetoresistance anomalies observed in present study in paramagnetic state of $ErB_{12}$ may be consistently interpreted in the framework of a simple relation between resistivity and magnetization -Δρ/ρ ~ $M^2$

Neural networks in Machine learning

В статті розглянуті основи роботи з нейронними мережами, особливу увагу приділено моделі мережі під назвою «перцептрон», запровадженої Френком Розенблаттом. До того ж було розкрито тему найпоширеніших мов програмування, що дозволяють втілити нейронні мережі у життя, шляхом створення програмного забезпечення, пов`язаного з ними.The paper covers the basic principles of Neural Networks’ work. Special attention is paid to Frank Rosenblatt’s model of the network called “perceptron”. In addition, the article touches upon the main programming languages used to write software for Neural Networks