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類神經網路的極限在哪裡
這份文檔探討了類神經網路的能力與限制，包括單層與多層神經網路的效果、數據需求和可解釋性問題。它指出，儘管深度學習在許多方面表現良好，但並非萬能，且存在計算資源要求和非線性問題。文中引用了知名學者的觀點，以強調目前人工智慧的發展狀況及其局限性。
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類神經網路的極限在哪裡
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- 2. 白居奇 Santa Clara University,Math Major, Cs Minor 精算師 人工智慧學校工程師 股價分析專案 IBM&遠傳合辦的AI比賽獲得佳作 帶領過房價分析，車牌辨識，水汙染等專 案 chi@aiacademy.tw
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- 10. 女友想吃什麼函數 牛肉麵 義大利麵 Pizz a 漢堡 水餃鐵板燒 1day ago 2 days ago 3 days ago 4 days ago 5 days ago 6 days ago 牛肉麵 義大利麵 Pizz a 漢堡 水餃鐵板燒 Hardmax ?? 西式 中式
- 11. 女友想吃什麼函數 牛肉麵 義大利麵 Pizz a 漢堡 水餃鐵板燒 1day ago 2 days ago 3 days ago 4 days ago 5 days ago 6 days ago 牛肉麵 0% 義大利麵 98% Pizz a 98% 漢堡 98% 水餃 <0% 鐵板燒 0% Hardmax ?? -0.99+0.5+0.5+0.5+0+0 西式 中式
- 12. 女友想吃什麼函數 牛肉麵 義大利麵 Pizz a 漢堡 水餃鐵板燒 1day ago 2 days ago 3 days ago 4 days ago 5 days ago 6 days ago 牛肉麵 43% 義大利麵 <0% Pizz a 43% 漢堡 43% 水餃 <0% 鐵板燒 43% Hardmax ?? 西式 中式 +0.5-0.85+0+0+0.5+0.5
- 13. 女友想吃什麼函數 牛肉麵 義大利麵 Pizz a 漢堡 水餃鐵板燒 1day ago 2 days ago 3 days ago 4 days ago 5 days ago 6 days ago 牛肉麵 51% 義大利麵 <0% Pizz a <0% 漢堡 26% 水餃 1% 鐵板燒 51% Hardmax ?? 西式 中式 +0.5+0-0.7+0+0.5+0.5
- 14. 女友想吃什麼函數 牛肉麵 義大利麵 Pizz a 漢堡 水餃鐵板燒 1day ago 2 days ago 3 days ago 4 days ago 5 days ago 6 days ago 牛肉麵 52% 義大利麵 <0% Pizz a <0% 漢堡 <0% 水餃 18% 鐵板燒 52% Hardmax ?? 西式 中式 +0.5+0+0-0.55+0.5+0.5
- 15. 女友想吃什麼函數 牛肉麵 義大利麵 Pizz a 漢堡 水餃鐵板燒 1day ago 2 days ago 3 days ago 4 days ago 5 days ago 6 days ago 牛肉麵 27% 義大利麵 4% Pizz a 7% 漢堡 13% 水餃 12% 鐵板燒 36% Hardmax ?? 西式 中式 +0+0.5+0.5+0.5-0.4+0
- 16. 女友想吃什麼函數 牛肉麵 義大利麵 Pizz a 漢堡 水餃鐵板燒 1day ago 2 days ago 3 days ago 4 days ago 5 days ago 6 days ago 牛肉麵 21% 義大利麵 12% Pizz a 15% 漢堡 20% 水餃 10% 鐵板燒 23% Hardmax 鐵板燒！ 西式 中式 +0+0.5+0.5+0.5+0-0.25
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- 18. These are notfunctions!!!!!!
- 19. This is afunction
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- 24. Math… Math… MATH!! In1983, Kurt Hornik, Maxwell Stinchombe, and Halbert White wrote a proof that claims you can represent any function using an single layer neural network (given that you have enough neurons). However…..
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- 27. Today's Goal Approximate This!Approach Use multiple rectangles to approximate the function on the left!
- 28. First Neuron Creates this! SecondNeuron Creates this! Why Two Neurons?
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- 30. Combining the LittleBumps w1 b1 w2 b2 w3 b3 w3 b3 w4 b4 w5 b5 w6 b6 w7 b7 w8 b8
- 31. Combining the LittleBumps w1 b1 w2 b2 w3 b3 w3 b3 w4 b4 w5 b5 w6 b6 w7 b7 w8 b8
- 32. Combining the LittleBumps w1 b1 w2 b2 w3 b3 w3 b3 w4 b4 w5 b5 w6 b6 w7 b7 w8 b8
- 33. Combining the LittleBumps w1 b1 w2 b2 w3 b3 w3 b3 w4 b4 w5 b5 w6 b6 w7 b7 w8 b8
- 34. Combining the LittleBumps w1 b1 w2 b2 w3 b3 w3 b3 w4 b4 w5 b5 w6 b6 w7 b7 w8 b8
- 35. Combining the LittleBumps w1 b1 w2 b2 w3 b3 w3 b3 w4 b4 w5 b5 w6 b6 w7 b7 w8 b8
- 36. Combining the LittleBumps w1 b1 w2 b2 w3 b3 w3 b3 w4 b4 w5 b5 w6 b6 w7 b7 w8 b8
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- 38. If one layerNN is so good, why does no one use it?
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- 40. 1 1, 2, 3, 4 1,2, 3, 4 1 1, 2, 3, 4 1, 2, 3, 4 Input Layer Hidden Layer Output Layer Input Layer Hidden Layer Output Layer Output Possibility: 2, 3, 4, 5, 6, 7, 8 Output Possibility: 1, 2, 3, 4, 6, 8, 9, 12, 16
- 41. The Deeper TheBetter?
- 44. Gradient Vanished Computing Resource DemandingNon Linearity NeedsEnormous Amount of Data How Does It Work Data Not Including the Entire Domain
- 45. Gradient Vanished Computing Resource DemandingNon Linearity NeedsEnormous Amount of Data How Does It Work Data Not Including the Entire Domain
- 46. How Does ItWork?
- 48. If your modelgives a result like this, then maybe you need some explanations….
- 49. What if anAI program tells you that you need surgery, but doesn't tell you why?
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- 52. Data Not Including theEntire Domain?
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- 56. Generated Faces That is nota face! Shit… I got caught...
- 57. AI solved manyproblems today, but can it truly solve everything?
- 58. Gary Marcus, aprofessor of cognitive psychology at NYU and briefly director of Uber’s AI lab “a universal solvent, but one tool among many.” Only good at pattern recognition
- 59. Nah Geoffrey Hinton, aprofessor emeritus at the University of Toronto and the grandfather of backpropagation. Bascially God of AI
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- 61. Deep Learning Today AIKnowledge
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