π Π‘ΠΎΠ·Π΄Π°Π½ΠΈΠ΅ ΠΏΡΠΈΠ»ΠΎΠΆΠ΅Π½ΠΈΠΉ ΠΌΠ°ΡΠΈΠ½Π½ΠΎΠ³ΠΎ ΠΎΠ±ΡΡΠ΅Π½ΠΈΡ. ΠΡ ΠΈΠ΄Π΅ΠΈ ΠΊ ΠΏΡΠΎΠ΄ΡΠΊΡΡ [2023] ΠΠΌΠΌΠ°Π½ΡΡΠ»Ρ ΠΠΌΠ΅ΠΉΠ·Π΅Π½
ΠΡΠ²ΠΎΠΉΡΠ΅ ΠΊΠ»ΡΡΠ΅Π²ΡΠ΅ Π½Π°Π²ΡΠΊΠΈ ΠΏΡΠΎΠ΅ΠΊΡΠΈΡΠΎΠ²Π°Π½ΠΈΡ, ΡΠ°Π·ΡΠ°Π±ΠΎΡΠΊΠΈ ΠΈ ΡΠ°Π·Π²Π΅ΡΡΡΠ²Π°Π½ΠΈΡ ΠΏΡΠΈΠ»ΠΎΠΆΠ΅Π½ΠΈΠΉ Π½Π° Π±Π°Π·Π΅ ΠΌΠ°ΡΠΈΠ½Π½ΠΎΠ³ΠΎ ΠΎΠ±ΡΡΠ΅Π½ΠΈΡ (ΠΠ)!
ΠΠΎΡΠ°Π³ΠΎΠ²ΠΎΠ΅ ΡΡΠΊΠΎΠ²ΠΎΠ΄ΡΡΠ²ΠΎ ΠΏΠΎ ΡΠΎΠ·Π΄Π°Π½ΠΈΡ ΠΠ-ΠΏΡΠΈΠ»ΠΎΠΆΠ΅Π½ΠΈΠΉ Ρ ΡΠΏΠΎΡΠΎΠΌ Π½Π° ΠΏΡΠ°ΠΊΡΠΈΠΊΡ: Π΄Π»Ρ ΡΠΏΠ΅ΡΠΈΠ°Π»ΠΈΡΡΠΎΠ² ΠΏΠΎ ΠΎΠ±ΡΠ°Π±ΠΎΡΠΊΠ΅ Π΄Π°Π½Π½ΡΡ , ΡΠ°Π·ΡΠ°Π±ΠΎΡΡΠΈΠΊΠΎΠ² ΠΏΡΠΎΠ³ΡΠ°ΠΌΠΌΠ½ΠΎΠ³ΠΎ ΠΎΠ±Π΅ΡΠΏΠ΅ΡΠ΅Π½ΠΈΡ ΠΈ ΠΏΡΠΎΠ΄Π°ΠΊΡ-ΠΌΠ΅Π½Π΅Π΄ΠΆΠ΅ΡΠΎΠ².
Π§ΠΈΡΠ°Ρ ΡΡΡ ΠΊΠ½ΠΈΠ³Ρ, Π²Ρ ΡΠ°Π³ Π·Π° ΡΠ°Π³ΠΎΠΌ ΡΠΎΠ·Π΄Π°Π΄ΠΈΡΠ΅ ΡΠ΅Π°Π»ΡΠ½ΠΎΠ΅ ΠΏΡΠ°ΠΊΡΠΈΡΠ΅ΡΠΊΠΎΠ΅ ΠΏΡΠΈΠ»ΠΎΠΆΠ΅Π½ΠΈΠ΅ β ΠΎΡ ΠΈΠ΄Π΅ΠΈ Π΄ΠΎ Π²Π½Π΅Π΄ΡΠ΅Π½ΠΈΡ. Π Π²Π°ΡΠ΅ΠΌ ΡΠ°ΡΠΏΠΎΡΡΠΆΠ΅Π½ΠΈΠΈ ΠΏΡΠΈΠΌΠ΅ΡΡ ΠΊΠΎΠ΄ΠΎΠ², ΠΈΠ»Π»ΡΡΡΡΠ°ΡΠΈΠΈ, ΡΠΊΡΠΈΠ½ΡΠΎΡΡ ΠΈ ΠΈΠ½ΡΠ΅ΡΠ²ΡΡ Ρ Π²Π΅Π΄ΡΡΠΈΠΌΠΈ ΡΠΏΠ΅ΡΠΈΠ°Π»ΠΈΡΡΠ°ΠΌΠΈ ΠΎΡΡΠ°ΡΠ»ΠΈ. ΠΡ Π½Π°ΡΡΠΈΡΠ΅ΡΡ ΠΏΠ»Π°Π½ΠΈΡΠΎΠ²Π°ΡΡ ΠΈ ΠΈΠ·ΠΌΠ΅ΡΡΡΡ ΡΡΠΏΠ΅Ρ ΠΠ-ΠΏΡΠΎΠ΅ΠΊΡΠΎΠ², ΡΠ°Π·Π±Π΅ΡΠ΅ΡΠ΅ΡΡ, ΠΊΠ°ΠΊ ΠΏΠΎΡΡΡΠΎΠΈΡΡ ΡΠ°Π±ΠΎΡΡΡ ΠΌΠΎΠ΄Π΅Π»Ρ, ΠΎΡΠ²ΠΎΠΈΡΠ΅ ΡΠΏΠΎΡΠΎΠ±Ρ Π΅Π΅ ΠΈΡΠ΅ΡΠ°ΡΠΈΠ²Π½ΠΎΠΉ Π΄ΠΎΡΠ°Π±ΠΎΡΠΊΠΈ. Π, Π½Π°ΠΊΠΎΠ½Π΅Ρ, ΠΏΠΎΠ·Π½Π°ΠΊΠΎΠΌΠΈΡΠ΅ΡΡ ΡΠΎ ΡΡΡΠ°ΡΠ΅Π³ΠΈΡΠΌΠΈ ΡΠ°Π·Π²Π΅ΡΡΡΠ²Π°Π½ΠΈΡ ΠΈ ΠΌΠΎΠ½ΠΈΡΠΎΡΠΈΠ½Π³Π°.
π Building Machine Learning Powered Applications: Going from Idea to Product [2020] Emmanuel Ameisen
Learn the skills necessary to design, build, and deploy applications powered by machine learning (ML). Through the course of this hands-on book, youβll build an example ML-driven application from initial idea to deployed product. Data scientists, software engineers, and product managersβincluding experienced practitioners and novices alikeβwill learn the tools, best practices, and challenges involved in building a real-world ML application step by step. Author Emmanuel Ameisen, an experienced data scientist who led an AI education program, demonstrates practical ML concepts using code snippets, illustrations, screenshots, and interviews with industry leaders. Part I teaches you how to plan an ML application and measure success. Part II explains how to build a working ML model. Part III demonstrates ways to improve the model until it fulfills your original vision. Part IV covers deployment and monitoring strategies.