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PART 1 ÇÁ·Î±×·¡¹Ö Áغñ ÀÛ¾÷
Chapter 01 °³¹ßȯ°æ ¼³Á¤Çϱâ
1.1 ¾Æ³ªÄÜ´Ù(Anaconda) ¼³Ä¡Çϱâ
1.1.1 À©µµ¿ì(Windows)¿¡¼ ¼³Ä¡Çϱâ
1.1.2 macOS¿¡¼ ¼³Ä¡Çϱâ
1.1.3 Å͹̳Î(Terminal) ½ÇÇà ¹æ¹ý
1.1.4 °³¹ßȯ°æ »ý¼º°ú »èÁ¦ ±×¸®°í ÆÐÅ°Áö ¼³Ä¡
1.1.5 °³¹ßȯ°æ È°¼ºÈ¿Í ºñÈ°¼ºÈ
1.1.6 °³¹ßȯ°æ ³»¿¡ ÆÐÅ°Áö ¼³Ä¡Çϱâ
1.1.7 °³¹ßȯ°æ ³»º¸³»±â¿Í ºÒ·¯¿À±â
1.2 ÅÙ¼Ç÷Î(TensorFlow) ¹× °ü·Ã ÆÐÅ°Áö ¼³Ä¡Çϱâ
1.2.1 ymlÀ» ÅëÇØ ºÒ·¯¿À±â
1.2.2 yml¾øÀÌ Á÷Á¢ ¼³Á¤Çϱâ
Chapter 02 ÁÖÇÇÅÍ ³ëÆ®ºÏ°ú ÆÄÀ̽ã Æ©Å丮¾ó
2.1 ÁÖÇÇÅÍ ³ëÆ®ºÏ(Jupyter Notebook)
2.1.1 ÆÄÀ̽ã ÄÚµå ½ÇÇàÇϱâ
2.1.2 ¸¶Å©´Ù¿î(Markdown)
2.1.3 Æí¸®ÇÑ ±â´É ¼Ò°³
2.2 ÆÄÀ̽㠱âÃÊ ¹®¹ý
2.2.1 º¯¼ö ¼±¾ð ¹× ÇÔ¼ö ¼±¾ð, ±×¸®°í À͸íÇÔ¼ö
2.2.2 ÁÖ¿ä º¯¼ö ŸÀÔ
2.2.3 for¹®(for loop)
2.2.4 if¹®(if statement)
2.2.5 Á¦³Ê·¹ÀÌÅÍ(Generator)
2.3 ÀÚÁÖ »ç¿ëµÇ´Â ÆÄÀ̽㠹®¹ý ÆÐÅÏ
2.3.1 µ¥ÀÌÅÍ Å¸ÀÔ¸¶´Ù ´Ù¸¥ for loop ½ºÅ¸ÀÏ
2.3.2 zip°¡ µé¾î°£ for loop
2.3.3 ÇÑ ÁÙ for¹®
2.3.4 ÆÄÀÏ Àбâ/¾²±â
2.4 numpy array
2.4.1 nÂ÷¿ø ¹è¿(Array)
2.4.2 ¹è¿ÀÇ ¸ð¾ç(Shape)
2.4.3 ÀüÄ¡ ¿¬»ê(Transpose)
2.4.4 Reshape
2.4.5 ¹è¿ À妽Ì
2.5 ½Ã°¢È ÆÐÅ°Áö(matplotlib) Æ©Å丮¾ó
2.5.1 ºÐÆ÷µµ(Scatter Plot) ±×¸®±â
2.5.2 Æä¾îÇöù(Pair Plot) ±×¸®±â
2.5.3 ´ÜÀϺ¯¼ö ÇÔ¼ö ±×·¡ÇÁ ±×¸®±â
2.5.4 ¿©·¯ ±×·¡ÇÁ¸¦ ÇÑ ´«¿¡ º¸±â
2.5.5 ±×·¡ÇÁ ½ºÅ¸Àϸµ
2.5.6 ´Ùº¯¼ö ÇÔ¼ö ±×·¡ÇÁ ±×¸®±â
Chapter 03 ÅÙ¼Ç÷ΠƩÅ丮¾ó
3.1 ÅÙ¼Ç÷Π¼³Ä¡
3.2 ÅÙ¼Ç÷Π±¸Á¶ ÀÌÇØÇϱâ
3.2.1 ±×·¡ÇÁ(Graph)
3.2.2 ÅÙ¼(Tensor)
3.2.3 ¿¬»ê(Operation)
3.3 ¿¬»êÀÇ ½ÃÀÛ ½ÃÁ¡
3.4 ÁÖ¿ä ŸÀÔ 3°¡Áö
3.4.1 Constant
3.4.2 Placeholder
3.4.3 Variable(º¯¼ö)
3.5 ±âÃÊ ¼öÇÐ ¿¬»ê
3.5.1 ½ºÄ®¶ó µ¡¼À
3.5.2 ÅÙ¼Ç÷ο¡¼ Á¦°øÇÏ´Â ´Ù¾çÇÑ ÇÔ¼ö
3.5.3 ¸®´ö¼Ç(Reduction)
PART 2 µö·¯´×¿¡ ÇÊ¿äÇÑ ¼öÄ¡Çؼ® ÀÌ·Ð
Chapter 04 ÃÖÀûÈ À̷п¡ ÇÊ¿äÇÑ ¼±Çü´ë¼ö¿Í ¹ÌºÐ
4.1 ¼±Çü´ë¼ö
4.1.1 ±³À°°úÁ¤¿¡ µû¸¥ ¼±Çü´ë¼öÀÇ ¹æÇ⼺
4.1.2 Á¤ÀÇ ¹× Ç¥±â¹ý
4.1.3 º¤ÅÍ/º¤ÅÍ ¿¬»ê
4.1.4 Çà·Ä/º¤ÅÍ ¿¬»ê
4.1.5 Çà·Ä/Çà·Ä ¿¬»ê
4.1.6 ¼±Çü½Ã½ºÅÛÀÇ Ç®ÀÌ
4.2 µö·¯´×¿¡¼ ÀÚÁÖ »ç¿ëµÇ´Â ¼±Çü´ë¼ö Ç¥±â¹ý
4.3 ¹ÌºÐ°ú ±×·¡µð¾ðÆ®(Gradient)
Chapter 05 µö·¯´×¿¡ ÇÊ¿äÇÑ ÃÖÀûÈ ÀÌ·Ð
5.1 µö·¯´×¿¡ ³ªÅ¸³ª´Â ÃÖÀûÈ ¹®Á¦
5.2 ÃÖÀûÈ ¹®Á¦ÀÇ Ãâ¹ß
5.3 ÃÖÀûÈ ¹®Á¦ Ç¥ÇöÀÇ µ¶Çعý
5.3.1 Á¦°ö°ªÀÇ ÇÕÀ» ÀÌ¿ëÇÑ ¼±Çüȸ±Í
5.3.2 Àý´ñ°ªÀÇ ÇÕÀ» »ç¿ëÇÑ ¼±Çüȸ±Í
5.4 ´Ù¾çÇÑ µö·¯´× ¸ðµ¨°ú ÃÖÀûÈ ¹®Á¦ ¹Ì¸®º¸±â
Chapter 06 °íÀü ¼öÄ¡ÃÖÀûÈ ¾Ë°í¸®Áò
6.1 ¼öÄ¡ÃÖÀûÈ ¾Ë°í¸®ÁòÀÌ ÇÊ¿äÇÑ ÀÌÀ¯
6.2 ¼öÄ¡ÃÖÀûÈ ¾Ë°í¸®ÁòÀÇ ÆÐÅÏ
6.3 ±×·¡µð¾ðÆ® µð¼¾Æ®(Gradient Descent)
6.3.1 ¿¹Á¦·Î ¹è¿ì´Â ±×·¡µð¾ðÆ® µð¼¾Æ®
6.3.2 ±×·¡µð¾ðÆ® µð¼¾Æ® ¹æ¹ýÀÇ ÇÑ°èÁ¡
6.4 ±×·¡µð¾ðÆ® µð¼¾Æ®¸¦ »ç¿ëÇÑ ¼±Çüȸ±Í ¸ðµ¨ ÇнÀ
6.4.1 ¼±Çüȸ±Í ¹®Á¦ ¼ö½Ä ¼Ò°³
6.4.2 ±×·¡µð¾ðÆ® µð¼¾Æ® ¹æ¹ý Àû¿ë
6.4.3 ÇÑ°èÁ¡
Chapter 07 µö·¯´×À» À§ÇÑ ¼öÄ¡ÃÖÀûÈ ¾Ë°í¸®Áò
7.1 ½ºÅäij½ºÆ½ ¹æ¹ý(Stochastic method)
7.2 ½ºÅäij½ºÆ½ ¹æ¹ýÀÇ ÄÚµå ±¸Çö ÆÐÅÏ
7.3 Ž»ö ¹æÇâ ±â¹Ý ¾Ë°í¸®Áò
7.3.1 ½ºÅäij½ºÆ½ ±×·¡µð¾ðÆ® µð¼¾Æ® ¹æ¹ý
7.3.2 ¸ð¸àÅÒ/³×½ºÅ×·ÎÇÁ ¹æ¹ý
7.4 ÇнÀ·ü ±â¹Ý ¾Ë°í¸®Áò
7.4.1 ÀûÀÀÇü ÇнÀ·ü ¹æ¹ýÀÇ Çʿ伺
7.4.2 Adagrad
7.4.3 RMSProp(Root Mean Square Propagation)
7.4.4 Adam
PART 3 ÅÙ¼Ç÷θ¦ »ç¿ëÇÑ µö·¯´×ÀÇ ±âº» ¸ðµ¨ ÇнÀ
Chapter 08 ¼±Çüȸ±Í ¸ðµ¨
8.1 ¿¹Ãø ¸ðµ¨°ú ¼Õ½ÇÇÔ¼ö
8.2 °áÁ¤·ÐÀû ¹æ¹ý°ú ½ºÅäij½ºÆ½ ¹æ¹ý
8.2.1 °áÁ¤·ÐÀû ¹æ¹ý
8.2.2 ½ºÅäij½ºÆ½ ¹æ¹ý
8.3 ºñ¼±Çüȸ±Í ¸ðµ¨
8.3.1 ÀÌÂ÷ °î¼± µ¥ÀÌÅÍ
8.3.2 »ïÂ÷ °î¼± µ¥ÀÌÅÍ
8.3.3 »ï°¢ÇÔ¼ö °î¼± µ¥ÀÌÅÍ
8.4 ºñ¼±Çü Ư¼º°ª ÃßÁ¤ ¹æ¹ý°ú ½Å°æ¸Á ¸ðµ¨
Chapter 09 ¼±Çü ºÐ·ù ¸ðµ¨
9.1 ÀÌÇ× ºÐ·ù ¸ðµ¨
9.1.1 ¿¬¼Ó È®·ü ¸ðµ¨
9.1.2 ÃÖ´ë¿ìµµ¹ý°ú Å©·Î½º ¿£Æ®·ÎÇÇ
9.1.3 ¹Ì´Ï ¹èÄ¡ ¹æ¹ýÀ» ÅëÇÑ ¸ðµ¨ ÇнÀ
9.1.4 Ư¼º°ªÀ» ÀÌ¿ëÇÑ ºñ¼±Çü ºÐ·ù ¸ðµ¨
9.2 ´ÙÁß ºÐ·ù ¸ðµ¨
9.2.1 ¼ÒÇÁÆ®¸Æ½º(Softmax)
9.2.2 ¿ø-ÇÖ(One-hot) ÀÎÄÚµù
9.2.3 ´ÙÁß ºÐ·ù ¸ðµ¨ÀÇ Å©·Î½º ¿£Æ®·ÎÇÇ
9.2.4 ¹Ì´Ï ¹èÄ¡ ¹æ¹ýÀ» ÅëÇÑ ¸ðµ¨ ÇнÀ
9.2.5 MNIST
Chapter 10 ½Å°æ¸Á ȸ±Í ¸ðµ¨
10.1 ½Å°æ¸Á ¸ðµ¨ÀÇ Çʿ伺
10.2 ½Å°æ¸Á ¸ðµ¨ ¿ë¾î ¼Ò°³
10.3 ½Å°æ¸Á ¸ðµ¨ ±¸Çö
10.4 ½Å°æ¸Á ¸ðµ¨ÀÇ ´Ù¾çÇÑ Ç¥Çö
10.5 Ư¼º°ª ÀÚµ¿ ÃßÃâÀÇ ¿ø¸®
10.6 ½Å°æ¸Á ¸ðµ¨ÀÇ ´ÜÁ¡
Chapter 11 ½Å°æ¸Á ºÐ·ù ¸ðµ¨
11.1 ½Å°æ¸Á ºÐ·ù ¸ðµ¨ÀÇ Çʿ伺
11.2 ´Ù¾çÇÑ µ¥ÀÌÅÍ ºÐÆ÷¿Í ½Å°æ¸Á ºÐ·ù ¸ðµ¨
11.2.1 ½Å°æ¸Á ºÐ·ù ¸ðµ¨ ÇнÀ
11.2.2 üĿº¸µå ¿¹Á¦
11.2.3 ºÒ±ÔÄ¢ÇÑ µ¥ÀÌÅÍ ºÐÆ÷ ¿¹Á¦
11.3 ½Å°æ¸Á ºÐ·ù ¸ðµ¨ÀÇ ´Ù¾çÇÑ Ç¥Çö
11.4 MNIST ºÐ·ù ¹®Á¦
PART 4 ÇнÀ¿ë/Å×½ºÆ®¿ë µ¥ÀÌÅÍ¿Í ¾ð´õÇÇÆÃ/¿À¹öÇÇÆÃ
Chapter 12 ¾ð´õÇÇÆÃ/¿À¹öÇÇÆà ¼Ò°³
12.1 µö·¯´× ¸ðµ¨°ú ÇÔ¼ö
12.2 ÇнÀ¿ë µ¥ÀÌÅÍ¿Í Á¤´äÇÔ¼ö
12.3 Á¤´äÇÔ¼ö¿Í Å×½ºÆ®¿ë µ¥ÀÌÅÍ
12.4 ¾ð´õÇÇÆÃ/¿À¹öÇÇÆÃÀÇ 2°¡Áö ¿äÀÎ
Chapter 13 ¾ð´õÇÇÆÃÀÇ Áø´Ü°ú ÇØ°áÃ¥
13.1 ÇнÀ ¹Ýº¹ Ƚ¼ö Àç¼³Á¤
13.2 ÇнÀ·ü Àç¼³Á¤
13.3 ¸ðµ¨ º¹Àâµµ Áõ°¡
13.4 ¾ð´õÇÇÆÃµÈ ½Å°æ¸Á ºÐ·ù ¸ðµ¨
13.5 ¾ð´õÇÇÆà ¿ä¾à
Chapter 14 ¿À¹öÇÇÆÃÀÇ Áø´Ü°ú ÇØ°áÃ¥
14.1 ÇнÀ ¹Ýº¹ Ƚ¼ö ÁÙÀ̱â
14.2 Regularization ÇÔ¼ö Ãß°¡
14.2.1 L2 Regularization
14.2.2 L1 Regularization
14.3 µå·Ó¾Æ¿ô(Dropout)
14.4 ºÐ·ù ¹®Á¦
14.5 ±³Â÷°ËÁõ µ¥ÀÌÅÍÀÇ µîÀå
Chapter 15 ÅÙ¼º¸µå(TensorBoard) È°¿ë
15.1 ±×·¡ÇÁ ±×¸®±â
15.2 È÷½ºÅä±×·¥ ±×¸®±â
15.3 À̹ÌÁö ±×¸®±â
15.4 ½Å°æ¸Á ¸ðµ¨ ÇнÀ °úÁ¤¿¡ ÅÙ¼º¸µå Àû¿ëÇϱâ
Chapter 16 ¸ðµ¨ ÀúÀåÇϱâ¿Í ºÒ·¯¿À±â
16.1 ÀúÀåÇϱâ
16.2 ºÒ·¯¿À±â
16.3 ¿À¹öÇÇÆà Çö»ó ÇØ°á ÀÀ¿ë ¿¹Á¦
Chapter 17 µö·¯´× °¡À̵å¶óÀÎ
17.1 µö·¯´× ÇÁ·ÎÁ§Æ® ÁøÇà ¼ø¼
17.1.1 ¸ðµ¨°ú ¼Õ½ÇÇÔ¼ö ¼±ÅÃ
17.1.2 ¸ðµ¨ ÇнÀ ÁøÇà
17.1.3 ¾ð´õÇÇÆà ȮÀÎ
17.1.4 ¿À¹öÇÇÆà ȮÀÎ
17.1.5 ÃÖÁ¾ ¼º´É È®ÀÎ
17.2 µö·¯´× ÇнÀÀÇ ±Ùº»Àû ÇÑ°è
17.2.1 ¼Õ½ÇÇÔ¼ö¿¡´Â ÇнÀ¿ë µ¥ÀÌÅÍ»ÓÀÌ´Ù.
17.2.2 µ¥ÀÌÅÍ Àü󸮴 ¸Å¿ì Áß¿äÇÏ´Ù.
17.2.3 ¼Õ½ÇÇÔ¼ö¿Í Á¤È®µµ´Â ´Ù¸£´Ù.
17.2.4 Å×½ºÆ® µ¥ÀÌÅÍÀÇ ºÐÆ÷´Â ¿ÏÀüÈ÷ ¾Ë ¼ö ¾ø´Ù.
PART 5 µö·¯´× ¸ðµ¨
Chapter 18 CNN ¸ðµ¨
18.1 µö·¯´×(Deep Learning) À̶õ
18.2 CNN ¸ðµ¨ ¼Ò°³
18.3 Äܺ¼·ç¼Ç(Convolution)
18.3.1 Ä¿³Î(Kernel)/Filter
18.3.2 Strides
18.3.3 Padding
18.4 Max-Pooling
18.5 Dropout
18.6 ReLU È°¼º ÇÔ¼ö
18.6.1 »ç¶óÁö´Â ±×·¡µð¾ðÆ® ¹®Á¦
18.6.2 ¹®Á¦ÀÇ ÀÌÇØ
18.6.3 ¹®Á¦ÀÇ ¿øÀÎ
18.6.4 ÇØ°á
18.7 ÀÚµ¿ Ư¼º(Feature) ÃßÃâ
18.8 MNIST ¼ýÀÚ ºÐ·ù ¹®Á¦
18.8.1 µ¥ÀÌÅÍ ÈȾ±â
18.8.2 One-Hot ÀÎÄÚµù
18.8.3 CNN ¸ðµ¨ ±¸ÃàÇϱâ
18.8.4 ÃÖÀûÈ ¹®Á¦ ¼³Á¤
18.8.5 ÇÏÀÌÆÛ ÆĶó¹ÌÅÍ ¼³Á¤
18.8.6 ÇнÀ ½ÃÀÛ
18.8.7 Á¤È®µµ È®ÀÎ
18.8.8 Àüü ÄÚµå
Chapter 19 GAN(Generative Adversarial Networks) ¸ðµ¨
19.1 min-max ÃÖÀûÈ ¹®Á¦ ¼Ò°³
19.2 Generator(»ý¼º±â)
19.2.1 Variable Scope(º¯¼ö ¹üÀ§)
19.2.2 Leaky ReLU(´©¼³ ReLU)
19.2.3 Tanh Output
19.3 Discriminator(ÆǺ°±â)
19.4 GAN ³×Æ®¿öÅ© ¸¸µé±â
19.4.1 Hyper parameters
19.5 ¼Õ½ÇÇÔ¼ö
19.6 Training(ÇнÀ)
19.6.1 Training(ÇнÀ)ÀÇ ¼¼ºÎ Á¶°Ç ¼³Á¤
19.6.2 Training loss(ÇнÀ ¼Õ½Ç)
19.6.3 »ý¼º±â·Î ¸¸µç »ùÇà ¿µ»ó
19.6.4 »ý¼º±â·Î »õ·Î¿î ¿µ»ó ¸¸µé±â
19.7 À¯¿ëÇÑ ¸µÅ© ¹× Àüü ÄÚµå
19.7.1 À¯¿ëÇÑ ¸µÅ©
19.7.2 Àüü ÄÚµå
PART 6 ÀÀ¿ë ¹®Á¦
Chapter 20 ¿µ»ó
20.1 Transfer Learning ¼Ò°³
20.2 ²É »çÁø ºÐ·ù
20.2.1 ÇÊ¿äÇÑ »çÀü Áö½Ä
20.2.2 ȯ°æ Áغñ
20.2.3 ¹®Á¦ ¼Ò°³
20.2.4 VGG16 ¸ðµ¨
20.2.5 µ¥ÀÌÅÍ ÈȾ±â
20.2.6 ¸ðµ¨ ¸¸µé±â
20.2.7 ÃÖÀûÈ ¹®Á¦ ¼³Á¤
20.2.8 ÇÏÀÌÆÛ ÆĶó¹ÌÅÍ ¼³Á¤
20.2.9 ÇнÀ
20.2.10 Á¤È®µµ
20.3 Bottleneck Ư¼º ÃßÃâ ¹æ¹ý
20.4 Transfer Learning Àüü ÄÚµå
Chapter 21 ¹®ÀÚ¿ ºÐ¼® word2vec
21.1 Word Embeddings
21.2 One-hot encoding
21.3 Word2Vec ¸ðµ¨
21.3.1 ȯ°æ Áغñ
21.3.2 Àüó¸®(preprocessing)
21.3.3 SubSampling
21.3.4 ¹èÄ¡ ¸¸µé±â
21.3.5 ±×·¡ÇÁ ¸¸µé±â
21.3.6 Embedding(ÀÓº£µù)
21.3.7 Negative sampling
21.3.8 Validation
21.3.9 Training ÇнÀ
21.4 T-SNE¸¦ ÀÌ¿ëÇÑ ½Ã°¢È
21.5 Àüü ÄÚµå
ã¾Æº¸±â
ÀÌ Ã¥Àº µö·¯´× ¸ðµ¨À» ½Ç¹«¿¡ Àû¿ëÇÏ¸ç ¾î·Á¿òÀ» °Þ¾ú´ø ½Ç¹«ÀÚ/¿¬±¸ÀÚÀÇ °æÇè°ú µö·¯´× °ÀǸ¦ ÁøÇàÇÏ¸ç ¹Þ¾Ò´ø ¸¹Àº Çǵå¹éÀ» Åä´ë·Î ¸¸µé¾ú½À´Ï´Ù. µö·¯´×ÀÇ ¿ø¸®¸¦ ÀÌÇØÇÒ ¼ö ÀÖµµ·Ï µö·¯´× À̷п¡ ´ëÇÑ ¼³¸í°ú ½Ç½À Äڵ带 µ¿½Ã¿¡ Á¦°øÇÕ´Ï´Ù.
ÀÌ·¯ÇÑ µö·¯´× ÀÌ·ÐÀ» ¹ÙÅÁÀ¸·Î, Ã¥ÀÇ ÈĹݺο¡¼´Â ½Ç¹«¿¡¼ È¿°úÀûÀ¸·Î »ç¿ëÇÒ ¼ö ÀÖ´Â µö·¯´× ¸ðµ¨À» ¼Ò°³ÇÔÀ¸·Î½á À̷п¡¸¸ Ä¡¿ìÄ¡Áö ¾Ê°í, ½Ç¹«¿¡µµ µµ¿òÀÌ µÇµµ·Ï ³»¿ëÀ» ±¸¼ºÇß½À´Ï´Ù.
¶ÇÇÑ, Ã¥¿¡ Æ÷ÇÔµÈ ±×·¡ÇÁ °á°ú¿Í µ¶ÀںеéÀÇ °á°ú°¡ Ç×»ó µ¿ÀÏÇÏ°Ô ³ª¿Ã ¼ö ÀÖµµ·Ï ½Å°æ ½è½À´Ï´Ù.
Ȥ½Ã Áú¹®À̳ª Äڵ尡 À߸øµÈ ºÎºÐÀÌ ÀÖ´Ù¸é https://github.com/DNRY/dlopt¿¡¼ ¼ÒÅëÇÒ ¼ö ÀÖµµ·Ï À¥ÆäÀÌÁö¸¦ °³¼³Çß½À´Ï´Ù.
±×¸®°í, Ã¥¿¡¼´Â ±×¸²ÀÌ Èæ¹éÀ¸·Î º¸ÀÔ´Ï´Ù. ÀúÀÚ À¥ÆäÀÌÁö¿¡¼´Â Ä÷¯ ±×¸²À» º¼ ¼ö ÀÖ½À´Ï´Ù.
[ÀÌ Ã¥ÀÇ ±¸¼º]
ÀÌ Ã¥Àº ´ÙÀ½°ú °°ÀÌ ÃÑ 6°¡Áö PART·Î ±¸¼ºµÇ¾î ÀÖ½À´Ï´Ù.
* PART 1: ÇÁ·Î±×·¡¹Ö Áغñ ÀÛ¾÷
* PART 2: µö·¯´×¿¡ ÇÊ¿äÇÑ ¼öÄ¡Çؼ® ÀÌ·Ð
* PART 3: ÅÙ¼Ç÷θ¦ »ç¿ëÇÑ µö·¯´×ÀÇ ±âº» ¸ðµ¨ ÇнÀ
* PART 4: ÇнÀ¿ë/Å×½ºÆ®¿ë µ¥ÀÌÅÍ¿Í ¾ð´õÇÇÆÃ/¿À¹öÇÇÆÃ
* PART 5: µö·¯´× ¸ðµ¨
* PART 6: ÀÀ¿ë ¹®Á¦
PART 1Àº °³¹ßȯ°æÀ» ¼³Á¤ÇÏ°í ÅÙ¼Ç÷ÎÀÇ ±âÃʸ¦ ¼³¸íÇÕ´Ï´Ù. PART 2¿¡¼´Â ÃÖÀûÈ ¹®Á¦¸¦ ¼³¸íÇÏ°í, ÃÖÀûÈ ¹®Á¦¸¦ Ǫ´Â ¾Ë°í¸®ÁòµéÀ» ¼Ò°³ÇÕ´Ï´Ù.
PART 3¿¡¼´Â µö·¯´×ÀÇ °¡Àå ±âº» ¸ðµ¨ÀÎ ¼±Çüȸ±Í/ºÐ·ù ¸ðµ¨°ú ½Å°æ¸Á ¸ðµ¨À» ÃÖÀûÈ ÀÌ·ÐÀ¸·Î ¼³¸íÇÏ°í, ÅÙ¼Ç÷ηΠ±¸ÇöÇÏ´Â ¹ýÀ» ¼Ò°³ÇÕ´Ï´Ù.
PART 4¿¡¼´Â µö·¯´× ¸ðµ¨¿¡¼ ÇÇÇÒ ¼ö ¾ø´Â ¾ð´õ/¿À¹öÇÇÆÃ(Under/Over fitting) ¹®Á¦¸¦ ¼Ò°³ÇÕ´Ï´Ù.
PART 5¿Í PART 6¿¡¼´Â PART 4±îÁö ´Ù·é ³»¿ëÀ» ¹ÙÅÁÀ¸·Î ½Ç¹«¿¡¼ »ç¿ë °¡´ÉÇÑ µö·¯´× ¸ðµ¨µéÀ» ¼Ò°³ÇÏ°í ÅÙ¼Ç÷ηΠ±¸ÇöÇÕ´Ï´Ù.
´ÙÀ½°ú °°ÀÌ µ¶ÀںеéÀÇ ÇнÀ ¼öÁØ¿¡ µû¶ó ´Ù¸¥ ÇнÀ ÆÐÅÏÀ¸·Î ÁøÇàÇÒ ¼ö ÀÖ½À´Ï´Ù.
* ÅÙ¼Ç÷θ¦ óÀ½ Á¢ÇÏ´Â ºÐ: PART 1--> PART 2 --> PART 3 --> PART 4 --> PART 5 --> PART 6
* ÅÙ¼Ç÷Π°æÇèÀÌ ÀÖ°í µö·¯´× ÀÌ·ÐÀÌ ºÎÁ·ÇÑ ºÐ: PART 2 --> PART 3 --> PART 4 --> PART 5 --> PART 6
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