Å×½ºÆ® ¼Â¿¡¼ÀÇ Á¤È®µµ°¡ ³ô´Ù´Â °ÍÀ» ÀǹÌÇÑ´Ù. ÀÌ°ÍÀ» ³ôÀ̱â À§Çؼ ¾î¶² ¹æ¹ýµéÀÌ ÀÖÀ»±î? 1. ¸¹Àº ¾çÀÇ µ¥ÀÌÅ͸¦ ÇнÀÇÑ´Ù. ¸¹Àº
¼º´ÉÀº KHADAS VIM3ÀÌ Raspberry Pi 4Bº¸´Ù ´õ ÁÁ½À´Ï´Ù. KHADAS VIM3´Â ARM CPU°¡ žÀçµÇ¾î ÀÖÀ¸¸ç NPU°¡ ÀåÂøµÇ¾î AI, µö·¯´×¿¡ ÃÖÀûÈµÈ Á¦Ç°ÀÔ´Ï´Ù. Raspberry Pi 4B´Â Broadcom BCM2711
¹®°ú»ýµµ ÀÌÇØÇÏ´Â µö·¯´× (8) – ½Å°æ¸Á ÇнÀ ÃÖÀûÈ 2017/09/27 – ¹®°ú»ýµµ ÀÌÇØÇÏ´Â µö·¯´× (1) – ÆÛ¼ÁÆ®·Ð Perceptron 2017/10/18 – ¹®°ú»ýµµ ÀÌÇØÇÏ´Â µö·¯´× (2) – ½Å°æ¸Á Neura
0/22/19/7344 A Method of Deep Learning Model Optimization for Image Classification on Edge Device Due to the recent increasing utilization of deep learning models on edge device
boostcourse > µö·¯´× ±âÃÊ´ÙÁö±â > 2. ÃÖÀûÈ Gradient Descent (°æ»ç ÇÏ°¹ý) 1Â÷·Î ¹ÌºÐÇÑ °ªÀ» »ç¿ëÇØ ¹Ýº¹ÀûÀ¸·Î ÃÖÀûÈÇÏ¿© local minimumÀ» ã´Â ¾Ë°í¸®Áò ¼±Çü ³×Æ®¿öÅ©, ÇÔ¼ö°¡
¹Þ¾Æ¾ßÇÏÁö¸¸, ½ÇÁ¦·Î´Â ¿ÞÂÊ ¸ðµ¨º¸´Ù ´õ ÀûÀº ¹úÁ¡À» ¹Þ¾Ò´Ù. ÀÌ·± »óȲÀÌ ¹ß»ýÇÏ´Â ÀÌÀ¯´Â ·ÎÁö½ºÆ½ ½Ã±×¸ðÀ̵å ÇÔ¼ö°¡ ´ÙÀ½°ú °°±â¿¡ ±×·¸´Ù. ÀÌ°É ÇØ°áÇϱâ À§ÇØ µö·¯´×¿¡¼´Â ±³Â÷
µö·¯´× ÃÖÀûÈ ±â¹ý¿¡´Â ¿©·¯°¡Áö°¡ ÀÖ½À´Ï´Ù. À̹øÀº RMSprop optimizer¿¡ ´ëÇØ ¾Ë¾Æº¸°Ú½À´Ï´Ù. RMSpropÀº ¿Ö ¾²Àϱî¿ä? ÃÖÀûȸ¦ ÇÒ ¶§ weight´Â °è½ïÇؼ ¾÷µ¥ÀÌÆ® µË´Ï´Ù. ¾÷µ¥ÀÌÆ®¸¦
– µ¥ÀÌÅÍ Àüó¸® – °¡ÁßÄ¡ ÃʱâÈ – ¸ð¸àÅÒ – ÀûÀÀÀû ÇнÀ·ü – È°¼ºÈ ÇÔ¼ö – ¹èÄ¡ Á¤±ÔÈ
µö·¯´× ÃÖÀûÈ ¾Ë°í¸®Áò(optimization algorithms) º» Æ÷½ºÆÿ¡¼´Â µö·¯´× ÃÖÀûÈ ¾Ë°í¸®Áò Áß¿¡¼ Momentum, Adam, ±×¸®°í RMSprop¿¡ ´ëÇؼ Á¤¸®ÇÏ´Â ½Ã°£À» °®µµ·Ï ÇÏ°Ú½À´Ï´Ù. Gradient
½ºÆ÷Ã÷Áß°è ¼Ö·ç¼Ç
½ºÆ÷Ã÷Áß°è api
½ºÆ÷Ã÷Áß°è»çÀÌÆ®Á¦ÀÛ
°³²´ÞÅä
ÅäÅä¼Ö·ç¼Ç Á¦ÀÛ
·ÑÅäÅä
°³²¾ß±¸Àå
½ºÆ÷Ã÷Áß°è
½ºÆ÷Ã÷Áß°è
·ÑÅäÅä
2 |