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πŸ“— Π’Π²Π΅Π΄Π΅Π½ΠΈΠ΅ Π² Π°Π²Ρ‚ΠΎΠΌΠ°Ρ‚ΠΈΠ·ΠΈΡ€ΠΎΠ²Π°Π½Π½ΠΎΠ΅ машинноС ΠΎΠ±ΡƒΡ‡Π΅Π½ΠΈΠ΅ [2023] Π₯ΡƒΡ‚Ρ‚Π΅Ρ€, ΠšΠΎΡ‚Ρ‚Ρ…ΠΎΡ„Ρ„, Π’Π°Π½ΡˆΠΎΡ€Π΅Π½.

ΠžΡˆΠ΅Π»ΠΎΠΌΠ»ΡΡŽΡ‰ΠΈΠΉ успСх коммСрчСских ΠΏΡ€ΠΈΠ»ΠΎΠΆΠ΅Π½ΠΈΠΉ машинного обучСния (machine learning – ML) ΠΈ быстрый рост этой отрасли создали высокий спрос Π½Π° Π³ΠΎΡ‚ΠΎΠ²Ρ‹Π΅ ΠΌΠ΅Ρ‚ΠΎΠ΄Ρ‹ ML, ΠΊΠΎΡ‚ΠΎΡ€Ρ‹Π΅ ΠΌΠΎΠΆΠ½ΠΎ Π»Π΅Π³ΠΊΠΎ ΠΈΡΠΏΠΎΠ»ΡŒΠ·ΠΎΠ²Π°Ρ‚ΡŒ Π±Π΅Π· ΡΠΏΠ΅Ρ†ΠΈΠ°Π»ΡŒΠ½Ρ‹Ρ… Π·Π½Π°Π½ΠΈΠΉ. Однако ΠΈ сСгодня успСх практичСского примСнСния Π² Ρ€Π΅ΡˆΠ°ΡŽΡ‰Π΅ΠΉ стСпСни зависит ΠΎΡ‚ экспСртов – людСй, ΠΊΠΎΡ‚ΠΎΡ€Ρ‹Π΅ Π²Ρ€ΡƒΡ‡Π½ΡƒΡŽ Π²Ρ‹Π±ΠΈΡ€Π°ΡŽΡ‚ подходящиС Π°Ρ€Ρ…ΠΈΡ‚Π΅ΠΊΡ‚ΡƒΡ€Ρ‹ ΠΈ ΠΈΡ… Π³ΠΈΠΏΠ΅Ρ€ΠΏΠ°Ρ€Π°ΠΌΠ΅Ρ‚Ρ€Ρ‹. ΠœΠ΅Ρ‚ΠΎΠ΄Ρ‹ AutoML Π½Π°Ρ†Π΅Π»Π΅Π½Ρ‹ Π½Π° устранСниС этого ΡƒΠ·ΠΊΠΎΠ³ΠΎ мСста ΠΏΡƒΡ‚Π΅ΠΌ построСния систСм ML, способных ΠΊ автоматичСской ΠΎΠΏΡ‚ΠΈΠΌΠΈΠ·Π°Ρ†ΠΈΠΈ ΠΈ самонастройкС нСзависимо ΠΎΡ‚ Ρ‚ΠΈΠΏΠ° Π²Ρ…ΠΎΠ΄Π½Ρ‹Ρ… Π΄Π°Π½Π½Ρ‹Ρ…. Π’ этой ΠΊΠ½ΠΈΠ³Π΅ Π²ΠΏΠ΅Ρ€Π²Ρ‹Π΅ прСдставлСн Π²ΡΠ΅ΠΎΠ±ΡŠΠ΅ΠΌΠ»ΡŽΡ‰ΠΈΠΉ ΠΎΠ±Π·ΠΎΡ€ Π±Π°Π·ΠΎΠ²Ρ‹Ρ… ΠΌΠ΅Ρ‚ΠΎΠ΄ΠΎΠ² Π°Π²Ρ‚ΠΎΠΌΠ°Ρ‚ΠΈΠ·ΠΈΡ€ΠΎΠ²Π°Π½Π½ΠΎΠ³ΠΎ машинного обучСния (AutoML). ИзданиС послуТит ΠΎΡ‚ΠΏΡ€Π°Π²Π½ΠΎΠΉ Ρ‚ΠΎΡ‡ΠΊΠΎΠΉ для изучСния этой быстро Ρ€Π°Π·Π²ΠΈΠ²Π°ΡŽΡ‰Π΅ΠΉΡΡ области; Ρ‚Π΅ΠΌ, ΠΊΡ‚ΠΎ ΡƒΠΆΠ΅ ΠΈΡΠΏΠΎΠ»ΡŒΠ·ΡƒΠ΅Ρ‚ AutoML Π² своСй Ρ€Π°Π±ΠΎΡ‚Π΅, ΠΊΠ½ΠΈΠ³Π° пригодится Π² качСствС справочника.

πŸ“˜ Automated Machine Learning: Methods, Systems, Challenges [2019] Frank Hutter, Lars Kotthoff, Joaquin Vanschoren

This open access book presents the first comprehensive overview of general methods in Automated Machine Learning (AutoML), collects descriptions of existing systems based on these methods, and discusses the first series of international challenges of AutoML systems. The recent success of commercial ML applications and the rapid growth of the field has created a high demand for off-the-shelf ML methods that can be used easily and without expert knowledge. However, many of the recent machine learning successes crucially rely on human experts, who manually select appropriate ML architectures (deep learning architectures or more traditional ML workflows) and their hyperparameters. To overcome this problem, the field of AutoML targets a progressive automation of machine learning, based on principles from optimization and machine learning itself.



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πŸ“— Π’Π²Π΅Π΄Π΅Π½ΠΈΠ΅ Π² Π°Π²Ρ‚ΠΎΠΌΠ°Ρ‚ΠΈΠ·ΠΈΡ€ΠΎΠ²Π°Π½Π½ΠΎΠ΅ машинноС ΠΎΠ±ΡƒΡ‡Π΅Π½ΠΈΠ΅ [2023] Π₯ΡƒΡ‚Ρ‚Π΅Ρ€, ΠšΠΎΡ‚Ρ‚Ρ…ΠΎΡ„Ρ„, Π’Π°Π½ΡˆΠΎΡ€Π΅Π½.

ΠžΡˆΠ΅Π»ΠΎΠΌΠ»ΡΡŽΡ‰ΠΈΠΉ успСх коммСрчСских ΠΏΡ€ΠΈΠ»ΠΎΠΆΠ΅Π½ΠΈΠΉ машинного обучСния (machine learning – ML) ΠΈ быстрый рост этой отрасли создали высокий спрос Π½Π° Π³ΠΎΡ‚ΠΎΠ²Ρ‹Π΅ ΠΌΠ΅Ρ‚ΠΎΠ΄Ρ‹ ML, ΠΊΠΎΡ‚ΠΎΡ€Ρ‹Π΅ ΠΌΠΎΠΆΠ½ΠΎ Π»Π΅Π³ΠΊΠΎ ΠΈΡΠΏΠΎΠ»ΡŒΠ·ΠΎΠ²Π°Ρ‚ΡŒ Π±Π΅Π· ΡΠΏΠ΅Ρ†ΠΈΠ°Π»ΡŒΠ½Ρ‹Ρ… Π·Π½Π°Π½ΠΈΠΉ. Однако ΠΈ сСгодня успСх практичСского примСнСния Π² Ρ€Π΅ΡˆΠ°ΡŽΡ‰Π΅ΠΉ стСпСни зависит ΠΎΡ‚ экспСртов – людСй, ΠΊΠΎΡ‚ΠΎΡ€Ρ‹Π΅ Π²Ρ€ΡƒΡ‡Π½ΡƒΡŽ Π²Ρ‹Π±ΠΈΡ€Π°ΡŽΡ‚ подходящиС Π°Ρ€Ρ…ΠΈΡ‚Π΅ΠΊΡ‚ΡƒΡ€Ρ‹ ΠΈ ΠΈΡ… Π³ΠΈΠΏΠ΅Ρ€ΠΏΠ°Ρ€Π°ΠΌΠ΅Ρ‚Ρ€Ρ‹. ΠœΠ΅Ρ‚ΠΎΠ΄Ρ‹ AutoML Π½Π°Ρ†Π΅Π»Π΅Π½Ρ‹ Π½Π° устранСниС этого ΡƒΠ·ΠΊΠΎΠ³ΠΎ мСста ΠΏΡƒΡ‚Π΅ΠΌ построСния систСм ML, способных ΠΊ автоматичСской ΠΎΠΏΡ‚ΠΈΠΌΠΈΠ·Π°Ρ†ΠΈΠΈ ΠΈ самонастройкС нСзависимо ΠΎΡ‚ Ρ‚ΠΈΠΏΠ° Π²Ρ…ΠΎΠ΄Π½Ρ‹Ρ… Π΄Π°Π½Π½Ρ‹Ρ…. Π’ этой ΠΊΠ½ΠΈΠ³Π΅ Π²ΠΏΠ΅Ρ€Π²Ρ‹Π΅ прСдставлСн Π²ΡΠ΅ΠΎΠ±ΡŠΠ΅ΠΌΠ»ΡŽΡ‰ΠΈΠΉ ΠΎΠ±Π·ΠΎΡ€ Π±Π°Π·ΠΎΠ²Ρ‹Ρ… ΠΌΠ΅Ρ‚ΠΎΠ΄ΠΎΠ² Π°Π²Ρ‚ΠΎΠΌΠ°Ρ‚ΠΈΠ·ΠΈΡ€ΠΎΠ²Π°Π½Π½ΠΎΠ³ΠΎ машинного обучСния (AutoML). ИзданиС послуТит ΠΎΡ‚ΠΏΡ€Π°Π²Π½ΠΎΠΉ Ρ‚ΠΎΡ‡ΠΊΠΎΠΉ для изучСния этой быстро Ρ€Π°Π·Π²ΠΈΠ²Π°ΡŽΡ‰Π΅ΠΉΡΡ области; Ρ‚Π΅ΠΌ, ΠΊΡ‚ΠΎ ΡƒΠΆΠ΅ ΠΈΡΠΏΠΎΠ»ΡŒΠ·ΡƒΠ΅Ρ‚ AutoML Π² своСй Ρ€Π°Π±ΠΎΡ‚Π΅, ΠΊΠ½ΠΈΠ³Π° пригодится Π² качСствС справочника.

πŸ“˜ Automated Machine Learning: Methods, Systems, Challenges [2019] Frank Hutter, Lars Kotthoff, Joaquin Vanschoren

This open access book presents the first comprehensive overview of general methods in Automated Machine Learning (AutoML), collects descriptions of existing systems based on these methods, and discusses the first series of international challenges of AutoML systems. The recent success of commercial ML applications and the rapid growth of the field has created a high demand for off-the-shelf ML methods that can be used easily and without expert knowledge. However, many of the recent machine learning successes crucially rely on human experts, who manually select appropriate ML architectures (deep learning architectures or more traditional ML workflows) and their hyperparameters. To overcome this problem, the field of AutoML targets a progressive automation of machine learning, based on principles from optimization and machine learning itself.

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