¿Ü±¹µµ¼
ÄÄÇ»ÅÍ
ÀÎÅͳÝ/À¥ °³¹ß
2013³â 9¿ù 9ÀÏ ÀÌÈÄ ´©Àû¼öÄ¡ÀÔ´Ï´Ù.
Á¤°¡ |
62,000¿ø |
---|
62,000¿ø
1,860P (3%Àû¸³)
ÇÒÀÎÇýÅÃ | |
---|---|
Àû¸³ÇýÅà |
|
|
|
Ãß°¡ÇýÅÃ |
|
À̺¥Æ®/±âȹÀü
¿¬°üµµ¼
»óÇ°±Ç
ÀÌ»óÇ°ÀÇ ºÐ·ù
¸ñÂ÷
1 An Introduction to Neural Networks.- 2 Machine Learning with Shallow Neural Networks.- 3 Training Deep Neural Networks.- 4 Teaching Deep Learners to Generalize.- 5 Radical Basis Function Networks.- 6 Restricted Boltzmann Machines.- 7 Recurrent Neural Networks.- 8 Convolutional Neural Networks.- 9 Deep Reinforcement Learning.- 10 Advanced Topics in Deep Learning.
Ã¥¼Ò°³
This book covers both classical and modern models in deep learning. The primary focus is on the theory and algorithms of deep learning. The theory and algorithms of neural networks are particularly important for understanding important concepts, so that one can understand the important design concepts of neural architectures in different applications. Why do neural networks work? When do they work better than off-the-shelf machine-learning models? When is depth useful? Why is training neural networks so hard? What are the pitfalls? The book is also rich in discussing different applications in order to give the practitioner a flavor of how neural architectures are designed for different types of problems. Applications associated with many different areas like recommender systems, machine translation, image captioning, image classification, reinforcement-learning based gaming, and text analytics are covered. The chapters of this book span three categories:
The basics of neural networks: Many traditional machine learning models can be understood as special cases of neural networks. An emphasis is placed in the first two chapters on understanding the relationship between traditional machine learning and neural networks. Support vector machines, linear/logistic regression, singular value decomposition, matrix factorization, and recommender systems are shown to be special cases of neural networks. These methods are studied together with recent feature engineering methods like word2vec.
Fundamentals of neural networks: A detailed discussion of training and regularization is provided in Chapters 3 and 4. Chapters 5 and 6 present radial-basis function (RBF) networks and restricted Boltzmann machines.
Advanced topics in neural networks: Chapters 7 and 8 discuss recurrent neural networks and convolutional neural networks. Several advanced topics like deep reinforcement learning, neural Turing machines, Kohonen self-organizing maps, and generative adversarial networks are introduced in Chapters 9 and 10.
The book is written for graduate students, researchers, and practitioners. Numerous exercises are available along with a solution manual to aid in classroom teaching. Where possible, an application-centric view is highlighted in order to provide an understanding of the practical uses of each class of techniques.
ÀúÀÚ¼Ò°³
»ý³â¿ùÀÏ | - |
---|
´º¿å ¿äũŸ¿î ÇÏÀÌÃ÷ÀÇ IBM T. J. ¿Ó½¼ ¸®¼Ä¡ ¼¾ÅÍÀÇ ¶Ù¾î³ ¿¬±¸ ȸ¿ø(DRSM)ÀÌ´Ù. 1993³â¿¡ IIT Kanpur¿¡¼ Çлç ÇÐÀ§¸¦ ¹Þ¾Ò°í, 1996³â¿¡ MIT¿¡¼ ¹Ú»ç ÇÐÀ§¸¦ ¹Þ¾Ò´Ù. µ¥ÀÌÅÍ ¸¶ÀÌ´× ºÐ¾ß¿¡¼ Æø³Ð°Ô ÀÏÇØ¿Ô°í, 400°³ ÀÌ»óÀÇ ³í¹®À» ÄÜÆÛ·±½º¿Í ÇмúÁö¿¡ ¹ßÇ¥ÇßÀ¸¸ç 80°³ ÀÌ»óÀÇ Æ¯Çã±ÇÀÌ ÀÖ´Ù. µ¥ÀÌÅÍ ¸¶À̴׿¡ °üÇÑ ±³°ú¼, ƯÀÌÄ¡ ºÐ¼®¿¡ °üÇÑ Æ÷°ýÀûÀΠåÀ» Æ÷ÇÔÇÑ 15±ÇÀÇ Ã¥À» Àú¼úÇϰųª ÆíÁýÇß´Ù. ƯÇãÀÇ »ó¾÷Àû °¡Ä¡ ´öºÐ¿¡ IBM¿¡¼ ¸¶½ºÅÍ ¹ß¸í°¡·Î ¼¼ ¹øÀ̳ª ÁöÁ¤µÆ´Ù. µ¥ÀÌÅÍ ½ºÆ®¸²ÀÇ »ý¹° Å×·¯¸®½ºÆ® À§Çù ŽÁö¿¡ ´ëÇÑ ¿¬±¸·Î IBM ±â¾÷»ó(2003)À» ¼ö»ó Çß°í, ÇÁ¶óÀ̹ö½Ã ±â¼ú¿¡ ´ëÇÑ °úÇÐÀûÀÎ °øÇåÀ¸·Î IBM ¿ì¼ö Çõ½Å»ó(2008)À» ¼ö»óÇß´Ù. µ¥ÀÌÅÍ ½ºÆ®¸² ¹× °íÂ÷¿øÀûÀÎ ÀÛ¾÷¿¡ ´ëÇÑ °¢°¢ÀÇ ÀÛ¾÷À» ÀÎÁ¤¹Þ¾Æ µÎ °³ÀÇ IBM ¿ì¼ö ±â¼ú ¼º°ú»ó(2009, 2015)À» ¼ö»óÇß´Ù. ÀÀÃà ±â¹Ý ÇÁ¶óÀ̹ö½Ã º¸Á¸ µ¥ÀÌÅÍ ¸¶À̴׿¡ ´ëÇÑ ¿¬±¸·Î EDBT 2014 Test of Time Award¸¦ ¼ö»óÇß´Ù. ¶ÇÇÑ µ¥ÀÌÅÍ ¸¶ÀÌ´× ºÐ¾ß¿¡¼ ¿µÇâ·Â ÀÖ´Â ¿¬±¸ °øÇå¿¡ ´ëÇÑ µÎ °¡Áö ÃÖ°í»ó Áß ÇϳªÀÎ IEEE ICDM ¿¬±¸ °øÇå»ó(2015)À» ¼ö»óÇß´Ù. IEEE ºòµ¥ÀÌÅÍ ÄÜÆÛ·±½º(2014)ÀÇ ÃÑ°ý °øµ¿ ÀÇÀåÁ÷°ú, ACM CIKM ÄÜÆÛ·±½º(2015), IEEE ICDM ÄÜÆÛ·±½º(2015), ACM KDD ÄÜÆÛ·±½º(2016) ÇÁ·Î±×·¥ °øµ¿ ÀÇÀåÁ÷À» ¿ªÀÓÇß´Ù. 2004³âºÎÅÍ 2008³â±îÁö ¡¸IEEE Transactions on Knowledge and Data Engineering¡¹ÀÇ ºÎÆíÁýÀåÀ¸·Î ±Ù¹«Çß´Ù. ¡¸ACM Transactions on Knowledge Discovery from Data¡¹ÀÇ ºÎÆíÁýÀå, ¡¸IEEE Transactions on Big Data¡¹ÀÇ ºÎÆíÁýÀå, ¡¸Data Mining and Knowledge Discovery Journal¡¹°ú ¡¸ACM SIGKDD Exploration¡¹ÀÇ ÆíÁýÀå, ¡¸Knowledge and Information Systems Journal¡¹ÀÇ ºÎÆíÁýÀåÀÌ´Ù. SpringerÀÇ °£Ç๰ÀÎ ¡¸Lecture Notes on Social Networks¡¹ ÀÚ¹® À§¿øȸ¿¡¼ È°µ¿ÇÏ°í ÀÖÀ¸¸ç µ¥ÀÌÅÍ ¸¶À̴׿¡ °üÇÑ SIAM È°µ¿ ±×·ìÀÇ ºÎ»çÀåÀ» ¿ªÀÓÇß´Ù. ¡°contributions to knowledge discovery and data mining algorithms¡±¿¡ °üÇÑ SIAM, ACM, IEEEÀÇ Æç·Î¿ì´Ù.
ÆîÃ帱âÀúÀÚÀÇ ´Ù¸¥Ã¥
Àüüº¸±âÁÖ°£·©Å·
´õº¸±â»óÇ°Á¤º¸Á¦°ø°í½Ã
À̺¥Æ® ±âȹÀü
ÄÄÇ»ÅÍ ºÐ¾ß¿¡¼ ¸¹Àº ȸ¿øÀÌ ±¸¸ÅÇÑ Ã¥
ÆǸÅÀÚÁ¤º¸
»óÈ£ |
(ÁÖ)±³º¸¹®°í |
---|---|
´ëÇ¥ÀÚ¸í |
¾Èº´Çö |
»ç¾÷ÀÚµî·Ï¹øÈ£ |
102-81-11670 |
¿¬¶ôó |
1544-1900 |
ÀüÀÚ¿ìÆíÁÖ¼Ò |
callcenter@kyobobook.co.kr |
Åë½ÅÆǸž÷½Å°í¹øÈ£ |
01-0653 |
¿µ¾÷¼ÒÀçÁö |
¼¿ïƯº°½Ã Á¾·Î±¸ Á¾·Î 1(Á¾·Î1°¡,±³º¸ºôµù) |
±³È¯/ȯºÒ
¹ÝÇ°/±³È¯ ¹æ¹ý |
¡®¸¶ÀÌÆäÀÌÁö > Ãë¼Ò/¹ÝÇ°/±³È¯/ȯºÒ¡¯ ¿¡¼ ½Åû ¶Ç´Â 1:1 ¹®ÀÇ °Ô½ÃÆÇ ¹× °í°´¼¾ÅÍ(1577-2555)¿¡¼ ½Åû °¡´É |
---|---|
¹ÝÇ°/±³È¯°¡´É ±â°£ |
º¯½É ¹ÝÇ°ÀÇ °æ¿ì Ãâ°í¿Ï·á ÈÄ 6ÀÏ(¿µ¾÷ÀÏ ±âÁØ) À̳»±îÁö¸¸ °¡´É |
¹ÝÇ°/±³È¯ ºñ¿ë |
º¯½É ȤÀº ±¸¸ÅÂø¿À·Î ÀÎÇÑ ¹ÝÇ°/±³È¯Àº ¹Ý¼Û·á °í°´ ºÎ´ã |
¹ÝÇ°/±³È¯ ºÒ°¡ »çÀ¯ |
·¼ÒºñÀÚÀÇ Ã¥ÀÓ ÀÖ´Â »çÀ¯·Î »óÇ° µîÀÌ ¼Õ½Ç ¶Ç´Â ÈÑ¼ÕµÈ °æ¿ì ·¼ÒºñÀÚÀÇ »ç¿ë, Æ÷Àå °³ºÀ¿¡ ÀÇÇØ »óÇ° µîÀÇ °¡Ä¡°¡ ÇöÀúÈ÷ °¨¼ÒÇÑ °æ¿ì ·º¹Á¦°¡ °¡´ÉÇÑ »óÇ° µîÀÇ Æ÷ÀåÀ» ÈѼÕÇÑ °æ¿ì ·½Ã°£ÀÇ °æ°ú¿¡ ÀÇÇØ ÀçÆǸŰ¡ °ï¶õÇÑ Á¤µµ·Î °¡Ä¡°¡ ÇöÀúÈ÷ °¨¼ÒÇÑ °æ¿ì ·ÀüÀÚ»ó°Å·¡ µî¿¡¼ÀÇ ¼ÒºñÀÚº¸È£¿¡ °üÇÑ ¹ý·üÀÌ Á¤ÇÏ´Â ¼ÒºñÀÚ Ã»¾àöȸ Á¦ÇÑ ³»¿ë¿¡ ÇØ´çµÇ´Â °æ¿ì |
»óÇ° Ç°Àý |
°ø±Þ»ç(ÃâÆÇ»ç) Àç°í »çÁ¤¿¡ ÀÇÇØ Ç°Àý/Áö¿¬µÉ ¼ö ÀÖÀ½ |
¼ÒºñÀÚ ÇÇÇغ¸»ó |
·»óÇ°ÀÇ ºÒ·®¿¡ ÀÇÇÑ ±³È¯, A/S, ȯºÒ, Ç°Áúº¸Áõ ¹× ÇÇÇغ¸»ó µî¿¡ °üÇÑ »çÇ×Àº¼ÒºñÀÚºÐÀïÇØ°á ±âÁØ (°øÁ¤°Å·¡À§¿øȸ °í½Ã)¿¡ ÁØÇÏ¿© ó¸®µÊ ·´ë±Ý ȯºÒ ¹× ȯºÒÁö¿¬¿¡ µû¸¥ ¹è»ó±Ý Áö±Þ Á¶°Ç, ÀýÂ÷ µîÀº ÀüÀÚ»ó°Å·¡ µî¿¡¼ÀǼҺñÀÚ º¸È£¿¡ °üÇÑ ¹ý·ü¿¡ µû¶ó ó¸®ÇÔ |
(ÁÖ)ÀÎÅÍÆÄÅ©Ä¿¸Ó½º´Â ȸ¿ø´ÔµéÀÇ ¾ÈÀü°Å·¡¸¦ À§ÇØ ±¸¸Å±Ý¾×, °áÁ¦¼ö´Ü¿¡ »ó°ü¾øÀÌ (ÁÖ)ÀÎÅÍÆÄÅ©Ä¿¸Ó½º¸¦ ÅëÇÑ ¸ðµç °Å·¡¿¡ ´ëÇÏ¿©
(ÁÖ)KGÀ̴Ͻýº°¡ Á¦°øÇÏ´Â ±¸¸Å¾ÈÀü¼ºñ½º¸¦ Àû¿ëÇÏ°í ÀÖ½À´Ï´Ù.
¹è¼Û¾È³»
±³º¸¹®°í »óÇ°Àº Åùè·Î ¹è¼ÛµÇ¸ç, Ãâ°í¿Ï·á 1~2Àϳ» »óÇ°À» ¹Þ¾Æ º¸½Ç ¼ö ÀÖ½À´Ï´Ù.
Ãâ°í°¡´É ½Ã°£ÀÌ ¼·Î ´Ù¸¥ »óÇ°À» ÇÔ²² ÁÖ¹®ÇÒ °æ¿ì Ãâ°í°¡´É ½Ã°£ÀÌ °¡Àå ±ä »óÇ°À» ±âÁØÀ¸·Î ¹è¼ÛµË´Ï´Ù.
±ººÎ´ë, ±³µµ¼Ò µî ƯÁ¤±â°üÀº ¿ìü±¹ Åù踸 ¹è¼Û°¡´ÉÇÕ´Ï´Ù.
¹è¼Ûºñ´Â ¾÷ü ¹è¼Ûºñ Á¤Ã¥¿¡ µû¸¨´Ï´Ù.