Applicaton example: Photo OCR Problem description and pipeline Photo OCR pipeline： Text...
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18. Applicaton example: Photo OCR
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16. Recommender Systems
Recommender Systems Problem formulation Content-based recommendations Optimization obje...
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17. Large scale machine learning
Large scale machine learning Learining with large datasets Stochastic gradient descent ...
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15. Anomaly detection
Anomaly detection Problem motivation Gaussian distribution Gaussian distribution: Say ....
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14. Dimensionality Reduction
Dimensionality Reduction Motivation I: Data compression Reduce data from 2D to 1D: Redu...
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Kali從入門到銀手鐲（三）：利用Metasploit工具的Win7滲透測試現(xiàn)在很多用戶還在使用Win7系統(tǒng)，雖然我個(gè)人也非常喜歡Win7，也承認(rèn)這是一款非常經(jīng)典的系統(tǒng)。但是Win7已經(jīng)在今年1月份徹底停止了支持，也就是說永遠(yuǎn)也不會得到安全更新和補(bǔ)丁...
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13. Clustering
Clustering Unsupervised learing introduction K-means algorithm Input: K (number of clus...
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12. Support Vector Machines
Support Vector Machines Optimization objective SVM hypothesis:logistic regression: cost...
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11. Maching learing system design
Maching learing system design Prioritizing what to work on: Spam classification example...
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10. Advice for applying machine learning
Advice for applying machine learning Deciding what to try next Debugging a learining al...
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9. Neural Networks: Learing
Neural Networks: Learing Cost Function L = total number of layers in network = no. of u...
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8. Neural Networks: Representation
Neural Networks: Representation Non-linear hypotheses Neural Networks. Neurons and the ...
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7. Overfitting
Overfitting The Problem of Overfitting undefitting(high bias): The algroithm doesn't fi...
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6. Classification
Classification Logistic Regression: Hypothesis Representation Want Hypothesis: Sigmoid ...
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5. Octave Tutorial(現(xiàn)在一般都用python做)
Octave Tutorial Octave 語法幾乎和Matelab一樣 一般的數(shù)學(xué)計(jì)算(elementary math operations)與python幾乎一樣：+、...
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4. Multiple features
Multiple features Multiple features Notation: n = number of features = input (features)...
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2. 代價(jià)函數(shù)
Cost Function Model Representation m = Number of training examplesx's = "input" variabl...
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