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Physics.Math.Code

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БообщСство Ρ„ΠΈΠ·ΠΈΠΊΠΎΠ², ΠΌΠ°Ρ‚Π΅ΠΌΠ°Ρ‚ΠΈΠΊΠΎΠ² ΠΈ Ρ€Π°Π·Ρ€Π°Π±ΠΎΡ‚Ρ‡ΠΈΠΊΠΎΠ². Книги, Π²ΠΈΠ΄Π΅ΠΎΡƒΡ€ΠΎΠΊΠΈ, ΡΡ‚Π°Ρ‚ΡŒΠΈ.

Physics.Math.Code

3 Π³ΠΎΠ΄Π° Π½Π°Π·Π°Π΄
ΠžΡ‚ΠΊΡ€Ρ‹Ρ‚ΡŒ Π²
πŸ“— Π‘ΠΎΠ·Π΄Π°Π½ΠΈΠ΅ ΠΏΡ€ΠΈΠ»ΠΎΠΆΠ΅Π½ΠΈΠΉ машинного обучСния. ΠžΡ‚ ΠΈΠ΄Π΅ΠΈ ΠΊ ΠΏΡ€ΠΎΠ΄ΡƒΠΊΡ‚Ρƒ [2023] Π­ΠΌΠΌΠ°Π½ΡƒΡΠ»ΡŒ АмСйзСн ΠžΡΠ²ΠΎΠΉΡ‚Π΅ ΠΊΠ»ΡŽΡ‡Π΅Π²Ρ‹Π΅ Π½Π°Π²Ρ‹ΠΊΠΈ проСктирования, Ρ€Π°Π·Ρ€Π°Π±ΠΎΡ‚ΠΊΠΈ ΠΈ развСртывания ΠΏΡ€ΠΈΠ»ΠΎΠΆΠ΅Π½ΠΈΠΉ Π½Π° Π±Π°Π·Π΅ машинного обучСния (МО)! ПошаговоС руководство ΠΏΠΎ созданию МО-ΠΏΡ€ΠΈΠ»ΠΎΠΆΠ΅Π½ΠΈΠΉ с ΡƒΠΏΠΎΡ€ΠΎΠΌ Π½Π° ΠΏΡ€Π°ΠΊΡ‚ΠΈΠΊΡƒ: для спСциалистов ΠΏΠΎ ΠΎΠ±Ρ€Π°Π±ΠΎΡ‚ΠΊΠ΅ Π΄Π°Π½Π½Ρ‹Ρ…, Ρ€Π°Π·Ρ€Π°Π±ΠΎΡ‚Ρ‡ΠΈΠΊΠΎΠ² ΠΏΡ€ΠΎΠ³Ρ€Π°ΠΌΠΌΠ½ΠΎΠ³ΠΎ обСспСчСния ΠΈ ΠΏΡ€ΠΎΠ΄Π°ΠΊΡ‚-ΠΌΠ΅Π½Π΅Π΄ΠΆΠ΅Ρ€ΠΎΠ². Читая эту ΠΊΠ½ΠΈΠ³Ρƒ, Π²Ρ‹ шаг Π·Π° шагом создадитС Ρ€Π΅Π°Π»ΡŒΠ½ΠΎΠ΅ практичСскоС ΠΏΡ€ΠΈΠ»ΠΎΠΆΠ΅Π½ΠΈΠ΅ β€” ΠΎΡ‚ ΠΈΠ΄Π΅ΠΈ Π΄ΠΎ внСдрСния. Π’ вашСм распоряТСнии ΠΏΡ€ΠΈΠΌΠ΅Ρ€Ρ‹ ΠΊΠΎΠ΄ΠΎΠ², ΠΈΠ»Π»ΡŽΡΡ‚Ρ€Π°Ρ†ΠΈΠΈ, ΡΠΊΡ€ΠΈΠ½ΡˆΠΎΡ‚Ρ‹ ΠΈ ΠΈΠ½Ρ‚Π΅Ρ€Π²ΡŒΡŽ с Π²Π΅Π΄ΡƒΡ‰ΠΈΠΌΠΈ спСциалистами отрасли. Π’Ρ‹ Π½Π°ΡƒΡ‡ΠΈΡ‚Π΅ΡΡŒ ΠΏΠ»Π°Π½ΠΈΡ€ΠΎΠ²Π°Ρ‚ΡŒ ΠΈ ΠΈΠ·ΠΌΠ΅Ρ€ΡΡ‚ΡŒ успСх МО-ΠΏΡ€ΠΎΠ΅ΠΊΡ‚ΠΎΠ², Ρ€Π°Π·Π±Π΅Ρ€Π΅Ρ‚Π΅ΡΡŒ, ΠΊΠ°ΠΊ ΠΏΠΎΡΡ‚Ρ€ΠΎΠΈΡ‚ΡŒ Ρ€Π°Π±ΠΎΡ‡ΡƒΡŽ модСль, освоитС способы Π΅Π΅ ΠΈΡ‚Π΅Ρ€Π°Ρ‚ΠΈΠ²Π½ΠΎΠΉ Π΄ΠΎΡ€Π°Π±ΠΎΡ‚ΠΊΠΈ. И, Π½Π°ΠΊΠΎΠ½Π΅Ρ†, ΠΏΠΎΠ·Π½Π°ΠΊΠΎΠΌΠΈΡ‚Π΅ΡΡŒ со стратСгиями развСртывания ΠΈ ΠΌΠΎΠ½ΠΈΡ‚ΠΎΡ€ΠΈΠ½Π³Π°. πŸ“˜ 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, you’ll build an example ML-driven application from initial idea to deployed product. Data scientists, software engineers, and product managersβ€”including experienced practitioners and novices alikeβ€”will 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.