By Π¨Π°Π»Π΅Π²-Π¨Π²Π°ΡΡ Π¨Π°ΠΉ , β¦
Π¨Π°Π»Π΅Π²-Π¨Π²Π°ΡΡ Π¨Π°ΠΉ, ΠΠ΅Π½-ΠΠ°Π²ΠΈΠ΄ Π¨Π°ΠΉ, 2019
ΠΠ°ΡΠΈΠ½Π½ΠΎΠ΅ ΠΎΠ±ΡΡΠ΅Π½ΠΈΠ΅ β Π±ΡΡΡΡΠΎ ΡΠ°Π·Π²ΠΈΠ²Π°ΡΡΠ°ΡΡΡ ΠΎΠ±Π»Π°ΡΡΡ ΠΈΠ½ΡΠΎΡΠΌΠ°ΡΠΈΠΊΠΈ Ρ ΡΠΈΡΠΎΠΊΠΈΠΌ ΡΠΏΠ΅ΠΊΡΡΠΎΠΌ ΠΏΡΠΈΠΌΠ΅Π½Π΅Π½ΠΈΠΉ. ΠΡΠ° ΠΊΠ½ΠΈΠ³Π° Π·Π½Π°ΠΊΠΎΠΌΠΈΡ Ρ ΠΎΡΠ½ΠΎΠ²ΠΎΠΏΠΎΠ»Π°Π³Π°ΡΡΠΈΠΌΠΈ ΠΏΡΠΈΠ½ΡΠΈΠΏΠ°ΠΌΠΈ ΠΈ Π°Π»Π³ΠΎΡΠΈΡΠΌΠΈΡΠ΅ΡΠΊΠΈΠΌΠΈ ΠΏΠΎΠ΄Ρ ΠΎΠ΄Π°ΠΌΠΈ Π² ΠΌΠ°ΡΠΈΠ½Π½ΠΎΠΌ ΠΎΠ±ΡΡΠ΅Π½ΠΈΠΈ. Π Π½Π΅ΠΉ ΠΏΡΠ΅Π΄ΡΡΠ°Π²Π»Π΅Π½Ρ ΡΠ΅ΠΎΡΠ΅ΡΠΈΡΠ΅ΡΠΊΠΈΠ΅ ΠΊΠΎΠ½ΡΠ΅ΠΏΡΠΈΠΈ ΠΈ ΠΌΠ°ΡΠ΅ΠΌΠ°ΡΠΈΡΠ΅ΡΠΊΠΈΠ΅ ΠΎΡΠ½ΠΎΠ²Ρ, ΠΏΡΠ΅ΠΎΠ±ΡΠ°Π·ΡΡΡΠΈΠ΅ ΠΈΠ΄Π΅ΠΈ Π² ΠΏΡΠ°ΠΊΡΠΈΡΠ΅ΡΠΊΠΈΠ΅ Π°Π»Π³ΠΎΡΠΈΡΠΌΡ. Π Π°ΡΡΠΌΠ°ΡΡΠΈΠ²Π°ΡΡΡΡ ΡΠ°ΠΊΠΈΠ΅ ΡΠ΅ΠΌΡ, ΠΊΠ°ΠΊ Π²ΡΡΠΈΡΠ»ΠΈΡΠ΅Π»ΡΠ½Π°Ρ ΡΠ»ΠΎΠΆΠ½ΠΎΡΡΡ ΠΎΠ±ΡΡΠ΅Π½ΠΈΡ, Π²ΡΠΏΡΠΊΠ»ΠΎΡΡΡ, ΡΡΡΠΎΠΉΡΠΈΠ²ΠΎΡΡΡ, ΡΡΠΎΡ Π°ΡΡΠΈΡΠ΅ΡΠΊΠΈΠΉ Π³ΡΠ°Π΄ΠΈΠ΅Π½ΡΠ½ΡΠΉ ΡΠΏΡΡΠΊ, Π½Π΅ΠΉΡΠΎΠ½Π½ΡΠ΅ ΡΠ΅ΡΠΈ ΠΈ ΡΡΡΡΠΊΡΡΡΠΈΡΠΎΠ²Π°Π½Π½ΡΠΉ Π²ΡΠ²ΠΎΠ΄.
Shai Shalev-Shwartz, Shai Ben-David, 2019
Machine learning is a rapidly advancing field in computer science with diverse applications. This book introduces readers to the fundamental principles and algorithmic paradigms of machine learning. It provides a comprehensive set of foundational theoretical ideas and mathematical derivations that transform these concepts into practical algorithms. The text covers topics such as computational learning complexity, convexity, stability, stochastic gradient descent, neural networks, and structured output learning.