µ¿¹æºÏ½º : ¿µ¾îÃ¥¿Â¶óÀμ­Á¡

ÇöÀç À§Ä¡

Ȩ

Non-Fiction [³íÇȼÇ]

È®´ë
SNS °øÀ¯
SNS °øÀ¯Çϱâ

  • ÆäÀ̽ººÏ

  • Æ®À§ÅÍ

  • īī¿ÀÅå
65%¡é
¡Ø ¼öÀÔµµ¼­ Ư¼º»ó ÁÖ¹®¿Ï·á ÈÄ¿¡µµ ǰÀýÀÌ ÀÖÀ» ¼ö ÀÖ½À´Ï´Ù.
ǰÀý ½Ã °³º°¿¬¶ôµå¸®°í ÀÖÀ¸¸ç, Àç°í Á¤È®µµ¸¦ À§ÇØ ³ë·ÂÇϰí ÀÖÀ¸´Ï ³Ê±×·¯¿î ¾çÇØ ºÎŹ µå¸³´Ï´Ù.

¡Ø ´ë·® ÁÖ¹® ½Ã ÁÖ¹® Àü Àç°í È®ÀÎÀ» ¿øÇÏ½Ã¸é ¾Æ·¡ °í°´¼¾ÅÍ·Î ¿¬¶ô¹Ù¶ø´Ï´Ù.
1:1¹®Àdzª Ä«Åå ¹®ÀÇ ÁÖ½Ã¸é ½Å¼ÓÇÏ°Ô ¾È³»ÇØ µå¸®°Ú½À´Ï´Ù.

1:1¹®ÀÇ   > Ä«Åå¹®ÀÇ   >

[ÆÄº»:B±Þ]Why Machines Learn: The Elegant Math Behind Modern AI (Paperback, ¹Ì±¹ÆÇ)

¢Ã »ó±â µµ¼­´Â ÆÄ¼Õ µµ¼­·Î Á¦º»ºÒ·®, ½ºÅ©·¡Ä¡, ¿À¿°, Ä®ÀÚ±¹, ±¸±è, Âõ±è ¹× »êÈ­ ÀÛ¿ëÀ¸·Î º¯»öµÈ µµ¼­ÀÏ ¼ö ÀÖÀ¸¸ç Á¤»ó µµ¼­¿Í´Â Â÷À̰¡ ÀÖ½À´Ï´Ù. * ±³È¯ ¹× ¹Ýǰ Àý´ë ºÒ°¡

  • Á¤°¡
    25,500¿ø
  • ÆÇ¸Å°¡
    8,900¿ø
  • ±¸¸ÅÇýÅÃ

    Àû¸³±Ý : + 0¿ø

  • ¹è¼Ûºñ
    ÃÑ °áÁ¦±Ý¾×ÀÌ 30,000¿ø ¹Ì¸¸½Ã ¹è¼Ûºñ 2,500¿øÀÌ Ã»±¸µË´Ï´Ù.
    ±Ý¾×º°¹è¼Ûºñ
    0¿ø ÀÌ»ó ~ 30,000¿ø ¹Ì¸¸ 2,500¿ø
    30,000¿ø ÀÌ»ó ~ 0¿ø

    ¹è¼Ûºñ °è»ê ±âÁØ : ÆÇ¸Å°¡ + ¿É¼Ç°¡ + Ãß°¡»óǰ°¡ + ÅØ½ºÆ®¿É¼Ç°¡ - »óǰÇÒÀΰ¡ - »óǰÄíÆùÇÒÀΰ¡

    Á¶°Çº°¹è¼Û
    Áö¿ªÃß°¡¹è¼Ûºñ
    ÀÎõ Áß±¸/°­È­/¿ËÁø ¼¶Áö¿ª 4,500 ~ 6,000¿ø
    Ãæ³² ´çÁø/¼­»ê ¼¶Áö¿ª 4,000 ~ 7,000¿ø
    Ãæ³² º¸·É/ÅÂ¾È ¼¶Áö¿ª 5,000¿ø
    °æºÏ ¿ï¸ª±º ÀüÁö¿ª 5,000¿ø
    ºÎ»ê °­¼­±¸ ¼¶Áö¿ª 4,000¿ø
    °æ³² »çõ/Å뿵/°ÅÁ¦ ¼¶Áö¿ª 3,000 ~ 4,000¿ø
    ÀüºÏ ±º»ê/ºÎ¾È ¼¶Áö¿ª 5,000¿ø
    Àü³² ¿©¼ö/Áøµµ/½Å¾È ¼¶Áö¿ª 7,000 ~ 8,000¿ø
    Àü³² ¿Ïµµ/°íÈï/¸ñÆ÷ ¼¶Áö¿ª 5,000 ~ 7,000¿ø
    Àü³² ¿µ±¤/º¸¼º ¼¶Áö¿ª 4,000¿ø
    Á¦ÁÖ ÀüÁö¿ª 3,000¿ø
    Á¦ÁÖ ÃßÀÚ¸é 7,000¿ø
    Á¦ÁÖ ¿ìµµ 6,000¿ø
    ÁÖ¹®½Ã°áÁ¦(¼±°áÁ¦) Áö¿ªÃß°¡¹è¼Ûºñ
  • »óǰ¹øÈ£
    2713392
  • ISBN / ÄÚµå
    9780593185766u
  • ÀÛ°¡
  • ÃâÆÇ»ç
    Penguin Publishing Group
  • Ãâ°£ÀÏ
    2025-08-26
  • ±¸¼º/ÆÇÇü
    Paperback | 496 pages
  • Å©±â/Á¤º¸
    149 x 228 x 26 mm

      ÃÑ »óǰ ±Ý¾×

      0 ¿ø

      (»óǰ±Ý¾× ¿ø + ±âº»¿É¼Ç ¿ø )
      ¡á B±Þ Ä«Å×°í¸® µµ¼­(»óǰ¸í¿¡ 'ÆÄº»'Ç¥±â) ³×À̹öÆäÀÌ ±¸¸Å ºÒ°¡


      A rich, narrative explanation of the mathematics that has brought us machine learning and the ongoing explosion of artificial intelligence

      Machine learning systems are making life-altering decisions for us: approving mortgage loans, determining whether a tumor is cancerous, or deciding if someone gets bail. They now influence developments and discoveries in chemistry, biology, and physics—the study of genomes, extrasolar planets, even the intricacies of quantum systems. And all this before large language models such as ChatGPT came on the scene.

      We are living through a revolution in machine learning-powered AI that shows no signs of slowing down. This technology is based on relatively simple mathematical ideas, some of which go back centuries, including linear algebra and calculus, the stuff of seventeenth- and eighteenth-century mathematics. It took the birth and advancement of computer science and the kindling of 1990s computer chips designed for video games to ignite the explosion of AI that we see today. In this enlightening book, Anil Ananthaswamy explains the fundamental math behind machine learning, while suggesting intriguing links between artificial and natural intelligence. Might the same math underpin them both?

      As Ananthaswamy resonantly concludes, to make safe and effective use of artificial intelligence, we need to understand its profound capabilities and limitations, the clues to which lie in the math that makes machine learning possible.

      In a brand-new afterword exclusively in the paperback edition, Ananthaswamy dives into the Transformer architecture that makes large language models like ChatGPT possible and points to groundbreaking future directions enabled by the technology.

      ÆòÁ¡ Á¦¸ñ ÀÛ¼ºÀÚ ÀÛ¼ºÀÏ
      µî·ÏµÈ »óǰÈıⰡ ¾ø½À´Ï´Ù.
      ¹øÈ£ Á¦¸ñ ÀÛ¼ºÀÚ ÀÛ¼ºÀÏ ÁøÇà»óȲ
      µî·ÏµÈ »óǰ¹®Àǰ¡ ¾ø½À´Ï´Ù.

      ¹è¼Û¾È³»

      ¡Ø ¹è¼Û±â°£ : °áÁ¦ÀÏ·Î ºÎÅÍ 2 ~ 4 ÀÏ ¼Ò¿ä ( °øÈÞÀÏ Á¦¿Ü)

        - ±¹³»Á¦ÀÛ »çÀÔ»óǰ, DVD, µµ¼­/»ê°£Áö¿ªÀº 5 ~ 7 ÀÏ ¼Ò¿ä

       

      ¡Ø ¹è¼Ûºñ : 2,500 ¿ø ( ½Ç °áÁ¦±Ý¾× ±âÁØ 3 ¸¸¿øÀÌ»ó ±¸¸Å½Ã ¹«·á )

       -  Á¦ÁÖ ¹× ±âŸ µµ¼­Áö¿ª : µµ¼±·á( ±âº»¹è¼Ûºñ + 3,000 ¿ø ~ 8,000¿ø )

        -  ±ººÎ´ë ¹× ÇØ¿Ü¹è¼ÛÀº Áö¿øµÇÁö ¾Ê½À´Ï´Ù.

       

      ¡Ø Çù·ÂÅùè»ç :  CJ ´ëÇÑÅë¿î  http://www.doortodoor.co.kr/  

      ¢Ï 1588 - 1255 ( ¿ù ~ ±Ý 08:00 ~ 18:00 / Åä 09:00 ~ 13:00 )

      ±³È¯/¹Ýǰ ¾È³»

      ¡Ø »óǰ ¼ö·É ÈÄ 7 ÀÏ À̳»¿¡ °í°´¼¾Å͸¦ ÅëÇØ ½ÅûÇÏ½Ç ¼ö ÀÖ½À´Ï´Ù.

          (ÀüÈ­ / Ä«Åå / 1:1¹®ÀÇ )

        - À̺¥Æ® »óǰÀÏ °æ¿ì »çÀºÇ°µµ °°ÀÌ ¹Ý³³ÇØ ÁÖ¼Å¾ß È¯ºÒµË´Ï´Ù.

        - »óǰºÒ·®ÀÎ °æ¿ì ¹è¼Ûºñ¸¦ Æ÷ÇÔÇÑ Àü¾×ÀÌ È¯ºÒµË´Ï´Ù.

       

      ¡Ø Àü »óǰ ¹Ýǰ½Ã 5,000 ¿ø ( °í°´ºÎ´ã )

        - ºÎºÐ ¹Ýǰ½Ã, ÁÖ¹®±Ý¾×ÀÌ 3¸¸¿ø ÀÌ»óÀ̸é 2,500 ¿ø (°í°´ºÎ´ã)

          ÁÖ¹®±Ý¾×ÀÌ 3 ¸¸¿ø ¹Ì¸¸À̸é 5,000 ¿ø (°í°´ºÎ´ã)

       

      ¡Ø ¹Ýǰ / ±³È¯ ºÒ°¡´ÉÇÑ °æ¿ì

        - Æ÷ÀåµÈ µµ¼­, CD µîÀÇ Æ÷ÀåÀ» °³ºÀ ¹× ÈѼÕÇÑ °æ¿ì,

        - ´Ü±â°£¿¡ Çʵ¶ÀÌ °¡´ÉÇÑ µµ¼­

          ex) CD, ¿©Ç༭, ¸¸È­, ¿ä¸®Ã¥, Áöµµ, »çÁøÁý, ¿öÅ©ºÏ µî

       

      ¡Ø ¹ÝǰÁÖ¼Ò

       - ¼­¿ïƯº°½Ã ¼ºµ¿±¸ ¼º¼öÀÏ·Î 55(SKÅ×Å©³ëºôµù) ÁöÇÏ1Ãþ 101~102È£

       

      À̹ÌÁö È®´ëº¸±â

      [ÆÄº»:B±Þ]Why Machines Learn: The Elegant Math Behind Modern AI


      Item size chart »çÀÌÁî ±âÁØÇ¥

      ¡Ø »óǰ»çÀÌÁî Ä¡¼ö´Â Àç´Â ¹æ¹ý°ú À§Ä¡¿¡ µû¶ó 1~3cm ¿ÀÂ÷°¡ ÀÖÀ» ¼ö ÀÖ½À´Ï´Ù.

      ºñ¹Ð¹øÈ£ È®ÀÎ ´Ý±â