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001. Welcome to Machine Learning!.mp4
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9.1 MB
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001. Welcome to Machine Learning!.srt
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2.4 KB
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002. Welcome.mp4
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18.3 MB
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002. Welcome.srt
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9.5 KB
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003. What is Machine Learning.mp4
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11.4 MB
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003. What is Machine Learning.srt
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11 KB
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004. Supervised Learning.mp4
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16.7 MB
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004. Supervised Learning.srt
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18.9 KB
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005. Unsupervised Learning.mp4
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23.3 MB
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005. Unsupervised Learning.srt
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27.5 KB
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006. Model Representation.mp4
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11.4 MB
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006. Model Representation.srt
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9.6 KB
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007. Cost Function.mp4
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11.5 MB
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007. Cost Function.srt
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10.2 KB
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008. Cost Function - Intuition I.mp4
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15.5 MB
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008. Cost Function - Intuition I.srt
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11.7 KB
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009. Cost Function - Intuition II.mp4
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17 MB
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009. Cost Function - Intuition II.srt
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10.8 KB
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010. Gradient Descent.mp4
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18.7 MB
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010. Gradient Descent.srt
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16.3 KB
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011. Gradient Descent Intuition.mp4
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16.6 MB
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011. Gradient Descent Intuition.srt
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15.9 KB
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012. Gradient Descent For Linear Regression.mp4
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16.4 MB
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012. Gradient Descent For Linear Regression.srt
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13.4 KB
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013. Matrices and Vectors.mp4
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11.9 MB
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013. Matrices and Vectors.srt
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14.9 KB
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014. Addition and Scalar Multiplication.mp4
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9.3 MB
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014. Addition and Scalar Multiplication.srt
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11.3 KB
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015. Matrix Vector Multiplication.mp4
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18.9 MB
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015. Matrix Vector Multiplication.srt
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22.8 KB
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016. Matrix Matrix Multiplication.mp4
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16.3 MB
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016. Matrix Matrix Multiplication.srt
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13.7 KB
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017. Matrix Multiplication Properties.mp4
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12.2 MB
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017. Matrix Multiplication Properties.srt
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11.5 KB
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018. Inverse and Transpose.mp4
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17 MB
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018. Inverse and Transpose.srt
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19.9 KB
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019. Multiple Features.mp4
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11.6 MB
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019. Multiple Features.srt
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13.7 KB
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020. Gradient Descent for Multiple Variables.mp4
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7.6 MB
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020. Gradient Descent for Multiple Variables.srt
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6.4 KB
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021. Gradient Descent in Practice I - Feature Scaling.mp4
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12.9 MB
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021. Gradient Descent in Practice I - Feature Scaling.srt
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16 KB
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022. Gradient Descent in Practice II - Learning Rate.mp4
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12.6 MB
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022. Gradient Descent in Practice II - Learning Rate.srt
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12.5 KB
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023. Features and Polynomial Regression.mp4
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11.5 MB
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023. Features and Polynomial Regression.srt
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15 KB
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024. Normal Equation.mp4
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23.6 MB
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024. Normal Equation.srt
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29.5 KB
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025. Normal Equation Noninvertibility.mp4
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8.8 MB
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025. Normal Equation Noninvertibility.srt
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8.6 KB
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026. Working on and Submitting Programming Assignments.mp4
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9 MB
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026. Working on and Submitting Programming Assignments.srt
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4.3 KB
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027. Basic Operations.mp4
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24.9 MB
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027. Basic Operations.srt
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23.9 KB
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028. Moving Data Around.mp4
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29.5 MB
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028. Moving Data Around.srt
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26.9 KB
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029. Computing on Data.mp4
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19.8 MB
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029. Computing on Data.srt
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16.7 KB
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030. Plotting Data.mp4
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20.1 MB
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030. Plotting Data.srt
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16.3 KB
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031. Control Statements for, while, if statement.mp4
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23.9 MB
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031. Control Statements for, while, if statement.srt
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22 KB
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032. Vectorization.mp4
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22.3 MB
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032. Vectorization.srt
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17.3 KB
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033. Classification.mp4
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11.3 MB
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033. Classification.srt
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11.4 KB
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034. Hypothesis Representation.mp4
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11.2 MB
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034. Hypothesis Representation.srt
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9.6 KB
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035. Decision Boundary.mp4
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22.2 MB
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035. Decision Boundary.srt
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17.9 KB
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036. Cost Function.mp4
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15.8 MB
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036. Cost Function.srt
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13.4 KB
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037. Simplified Cost Function and Gradient Descent.mp4
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16.3 MB
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037. Simplified Cost Function and Gradient Descent.srt
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14 KB
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038. Advanced Optimization.mp4
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26.8 MB
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038. Advanced Optimization.srt
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26.3 KB
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039. Multiclass Classification One-vs-all.mp4
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9.1 MB
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039. Multiclass Classification One-vs-all.srt
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9.2 KB
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040. The Problem of Overfitting.mp4
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14.9 MB
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040. The Problem of Overfitting.srt
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18.2 KB
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041. Cost Function.mp4
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MP4
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15.5 MB
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041. Cost Function.srt
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18.6 KB
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042. Regularized Linear Regression.mp4
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MP4
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15.6 MB
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042. Regularized Linear Regression.srt
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14.2 KB
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043. Regularized Logistic Regression.mp4
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16.8 MB
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043. Regularized Logistic Regression.srt
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16.2 KB
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044. Non-linear Hypotheses.mp4
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14.7 MB
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044. Non-linear Hypotheses.srt
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18 KB
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045. Neurons and the Brain.mp4
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14.6 MB
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045. Neurons and the Brain.srt
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15.5 KB
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046. Model Representation I.mp4
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MP4
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18 MB
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046. Model Representation I.srt
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14.4 KB
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047. Model Representation II.mp4
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18.4 MB
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047. Model Representation II.srt
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21.1 KB
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048. Examples and Intuitions I.mp4
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10.1 MB
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048. Examples and Intuitions I.srt
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8.5 KB
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049. Examples and Intuitions II.mp4
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20.9 MB
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049. Examples and Intuitions II.srt
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SRT
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11.4 KB
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050. Multiclass Classification.mp4
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MP4
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7 MB
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050. Multiclass Classification.srt
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SRT
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7 KB
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051. Cost Function.mp4
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MP4
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10.2 MB
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051. Cost Function.srt
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SRT
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8.9 KB
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052. Backpropagation Algorithm.mp4
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MP4
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19.1 MB
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052. Backpropagation Algorithm.srt
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SRT
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21.5 KB
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053. Backpropagation Intuition.mp4
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22.2 MB
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053. Backpropagation Intuition.srt
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17.7 KB
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054. Implementation Note Unrolling Parameters.mp4
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MP4
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12.9 MB
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054. Implementation Note Unrolling Parameters.srt
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14 KB
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055. Gradient Checking.mp4
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MP4
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18.4 MB
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055. Gradient Checking.srt
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SRT
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17 KB
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056. Random Initialization.mp4
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MP4
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9.8 MB
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056. Random Initialization.srt
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10.3 KB
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057. Putting It Together.mp4
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23.5 MB
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057. Putting It Together.srt
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26.1 KB
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058. Autonomous Driving.mp4
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28.3 MB
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058. Autonomous Driving.srt
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6.9 KB
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059. Deciding What to Try Next.mp4
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MP4
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9.4 MB
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059. Deciding What to Try Next.srt
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SRT
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11.7 KB
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060. Evaluating a Hypothesis.mp4
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MP4
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11 MB
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060. Evaluating a Hypothesis.srt
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SRT
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10.9 KB
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061. Model Selection and Train Validation Test Sets.mp4
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MP4
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19 MB
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061. Model Selection and Train Validation Test Sets.srt
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SRT
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16.9 KB
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062. Diagnosing Bias vs. Variance.mp4
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MP4
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12.2 MB
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062. Diagnosing Bias vs. Variance.srt
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11.2 KB
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063. Regularization and Bias Variance.mp4
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16.4 MB
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063. Regularization and Bias Variance.srt
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SRT
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14.9 KB
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064. Learning Curves.mp4
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MP4
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16.4 MB
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064. Learning Curves.srt
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SRT
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23.3 KB
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065. Deciding What to Do Next Revisited.mp4
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11.4 MB
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065. Deciding What to Do Next Revisited.srt
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13.3 KB
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066. Prioritizing What to Work On.mp4
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15.1 MB
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066. Prioritizing What to Work On.srt
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SRT
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18.5 KB
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067. Error Analysis.mp4
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MP4
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21.3 MB
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067. Error Analysis.srt
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SRT
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19.3 KB
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068. Error Metrics for Skewed Classes.mp4
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MP4
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17.9 MB
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068. Error Metrics for Skewed Classes.srt
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SRT
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20.8 KB
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069. Trading Off Precision and Recall.mp4
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MP4
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21.3 MB
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069. Trading Off Precision and Recall.srt
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SRT
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19.7 KB
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070. Data For Machine Learning.mp4
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MP4
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17.3 MB
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070. Data For Machine Learning.srt
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SRT
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21.8 KB
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071. Optimization Objective.mp4
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MP4
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21.9 MB
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071. Optimization Objective.srt
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SRT
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19.8 KB
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072. Large Margin Intuition.mp4
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MP4
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15.2 MB
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072. Large Margin Intuition.srt
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SRT
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20.1 KB
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073. Mathematics Behind Large Margin Classification.mp4
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MP4
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28.5 MB
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073. Mathematics Behind Large Margin Classification.srt
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SRT
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33.8 KB
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074. Kernels I.mp4
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MP4
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22.8 MB
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074. Kernels I.srt
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SRT
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27.4 KB
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075. Kernels II.mp4
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MP4
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22.6 MB
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075. Kernels II.srt
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SRT
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29 KB
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076. Using An SVM.mp4
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MP4
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32 MB
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076. Using An SVM.srt
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SRT
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41.1 KB
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077. Unsupervised Learning Introduction.mp4
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MP4
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5.2 MB
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077. Unsupervised Learning Introduction.srt
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SRT
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5 KB
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078. K-Means Algorithm.mp4
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MP4
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17.7 MB
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078. K-Means Algorithm.srt
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SRT
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24.7 KB
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079. Optimization Objective.mp4
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MP4
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10.9 MB
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079. Optimization Objective.srt
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SRT
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9.3 KB
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080. Random Initialization.mp4
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MP4
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11.1 MB
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080. Random Initialization.srt
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SRT
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15.3 KB
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081. Choosing the Number of Clusters.mp4
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MP4
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12.2 MB
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081. Choosing the Number of Clusters.srt
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SRT
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16.9 KB
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082. Motivation I Data Compression.mp4
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MP4
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21.5 MB
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082. Motivation I Data Compression.srt
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SRT
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19 KB
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083. Motivation II Visualization.mp4
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MP4
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8.3 MB
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083. Motivation II Visualization.srt
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SRT
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9.6 KB
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084. Principal Component Analysis Problem Formulation.mp4
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MP4
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14 MB
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084. Principal Component Analysis Problem Formulation.srt
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SRT
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13 KB
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085. Principal Component Analysis Algorithm.mp4
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MP4
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24.3 MB
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085. Principal Component Analysis Algorithm.srt
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SRT
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26.9 KB
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086. Reconstruction from Compressed Representation.mp4
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MP4
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7.2 MB
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086. Reconstruction from Compressed Representation.srt
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SRT
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5.1 KB
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087. Choosing the Number of Principal Components.mp4
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MP4
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15.6 MB
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087. Choosing the Number of Principal Components.srt
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SRT
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19.9 KB
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088. Advice for Applying PCA.mp4
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MP4
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19.7 MB
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088. Advice for Applying PCA.srt
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SRT
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24.8 KB
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089. Problem Motivation.mp4
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MP4
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10.6 MB
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089. Problem Motivation.srt
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SRT
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15.1 KB
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090. Gaussian Distribution.mp4
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MP4
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15.2 MB
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090. Gaussian Distribution.srt
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SRT
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14.5 KB
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091. Algorithm.mp4
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MP4
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18.9 MB
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091. Algorithm.srt
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SRT
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22.1 KB
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092. Developing and Evaluating an Anomaly Detection System.mp4
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MP4
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20.5 MB
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092. Developing and Evaluating an Anomaly Detection System.srt
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SRT
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25.8 KB
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093. Anomaly Detection vs. Supervised Learning.mp4
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MP4
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13.1 MB
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093. Anomaly Detection vs. Supervised Learning.srt
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SRT
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11.2 KB
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094. Choosing What Features to Use.mp4
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MP4
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19.1 MB
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094. Choosing What Features to Use.srt
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SRT
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23.7 KB
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095. Multivariate Gaussian Distribution.mp4
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MP4
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21.9 MB
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095. Multivariate Gaussian Distribution.srt
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SRT
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25.8 KB
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096. Anomaly Detection using the Multivariate Gaussian Distribution.mp4
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MP4
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22.4 MB
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096. Anomaly Detection using the Multivariate Gaussian Distribution.srt
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SRT
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24.8 KB
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097. Problem Formulation.mp4
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MP4
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16.4 MB
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097. Problem Formulation.srt
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SRT
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15.9 KB
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098. Content Based Recommendations.mp4
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MP4
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23.2 MB
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098. Content Based Recommendations.srt
|
SRT
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19.5 KB
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099. Collaborative Filtering.mp4
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MP4
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15.5 MB
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099. Collaborative Filtering.srt
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SRT
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19.1 KB
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100. Collaborative Filtering Algorithm.mp4
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MP4
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14.7 MB
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100. Collaborative Filtering Algorithm.srt
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SRT
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15.6 KB
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101. Vectorization Low Rank Matrix Factorization.mp4
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MP4
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12.8 MB
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101. Vectorization Low Rank Matrix Factorization.srt
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SRT
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15.4 KB
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102. Implementational Detail Mean Normalization.mp4
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MP4
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12.9 MB
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102. Implementational Detail Mean Normalization.srt
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SRT
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15.6 KB
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103. Learning With Large Datasets.mp4
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MP4
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8.5 MB
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103. Learning With Large Datasets.srt
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SRT
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7.6 KB
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104. Stochastic Gradient Descent.mp4
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MP4
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21 MB
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104. Stochastic Gradient Descent.srt
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SRT
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17.6 KB
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105. Mini-Batch Gradient Descent.mp4
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MP4
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9.8 MB
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105. Mini-Batch Gradient Descent.srt
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SRT
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7.5 KB
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106. Stochastic Gradient Descent Convergence.mp4
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MP4
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18.1 MB
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106. Stochastic Gradient Descent Convergence.srt
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SRT
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15.7 KB
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107. Online Learning.mp4
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MP4
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20.5 MB
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107. Online Learning.srt
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SRT
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26.1 KB
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108. Map Reduce and Data Parallelism.mp4
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MP4
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21.2 MB
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108. Map Reduce and Data Parallelism.srt
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SRT
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27.2 KB
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109. Problem Description and Pipeline.mp4
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MP4
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10.4 MB
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109. Problem Description and Pipeline.srt
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SRT
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13.9 KB
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110. Sliding Windows.mp4
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MP4
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21.9 MB
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110. Sliding Windows.srt
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SRT
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29.7 KB
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111. Getting Lots of Data and Artificial Data.mp4
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MP4
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25.3 MB
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111. Getting Lots of Data and Artificial Data.srt
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SRT
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33.2 KB
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112. Ceiling Analysis What Part of the Pipeline to Work on Next.mp4
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MP4
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21.9 MB
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112. Ceiling Analysis What Part of the Pipeline to Work on Next.srt
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SRT
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21.8 KB
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113. Summary and Thank You.mp4
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MP4
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9.1 MB
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113. Summary and Thank You.srt
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7.7 KB
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[CourseClub.NET].url
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URL
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[FCS Forum].url
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URL
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[FreeCourseSite.com].url
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![[Coursera] Machine Learning](/static/img/no-torrent-image.png "[Coursera] Machine Learning")
