CourseClub NET Coursera Neural Networks and Deep Learning | 9D4AB63E818A10F7C0054D8DBE135E73F64AE06F

CourseClub NET Coursera Neural Networks and Deep Learning

Name	[CourseClub.NET] Coursera - Neural Networks and Deep Learning	DOWNLOAD Copy Link
Total Size	920.8 MB
Total Files	96
Hash	9D4AB63E818A10F7C0054D8DBE135E73F64AE06F

/001.Welcome to the Deep Learning Specialization/
001. Welcome.mp4	10.7 MB
001. Welcome.srt	9.0 KB
/002.Introduction to Deep Learning/
002. What is a neural network.mp4	10.5 MB
002. What is a neural network.srt	10.1 KB
003. Supervised Learning with Neural Networks.mp4	13.5 MB
003. Supervised Learning with Neural Networks.srt	12.2 KB
004. Why is Deep Learning taking off.mp4	19.6 MB
004. Why is Deep Learning taking off.srt	18.3 KB
005. About this Course.mp4	4.9 MB
005. About this Course.srt	4.5 KB
006. Course Resources.mp4	2.6 MB
006. Course Resources.srt	3.7 KB
/003.Heroes of Deep Learning (Optional)/
007. Geoffrey Hinton interview.mp4	201.1 MB
007. Geoffrey Hinton interview.srt	58.9 KB
/004.Logistic Regression as a Neural Network/
008. Binary Classification.mp4	16.0 MB
008. Binary Classification.srt	10.8 KB
009. Logistic Regression.mp4	8.9 MB
009. Logistic Regression.srt	7.7 KB
010. Logistic Regression Cost Function.mp4	13.8 MB
010. Logistic Regression Cost Function.srt	11.3 KB
011. Gradient Descent.mp4	17.9 MB
011. Gradient Descent.srt	15.7 KB
012. Derivatives.mp4	14.1 MB
012. Derivatives.srt	12.3 KB
013. More Derivative Examples.mp4	17.6 MB
013. More Derivative Examples.srt	13.2 KB
014. Computation graph.mp4	5.9 MB
014. Computation graph.srt	4.4 KB
015. Derivatives with a Computation Graph.mp4	22.7 MB
015. Derivatives with a Computation Graph.srt	16.7 KB
016. Logistic Regression Gradient Descent.mp4	11.7 MB
016. Logistic Regression Gradient Descent.srt	9.2 KB
017. Gradient Descent on m Examples.mp4	12.8 MB
017. Gradient Descent on m Examples.srt	12.6 KB
/005.Python and Vectorization/
018. Vectorization.mp4	13.2 MB
018. Vectorization.srt	9.8 KB
019. More Vectorization Examples.mp4	10.8 MB
019. More Vectorization Examples.srt	7.6 KB
020. Vectorizing Logistic Regression.mp4	12.0 MB
020. Vectorizing Logistic Regression.srt	9.8 KB
021. Vectorizing Logistic Regression's Gradient Output.mp4	16.3 MB
021. Vectorizing Logistic Regression's Gradient Output.srt	11.0 KB
022. Broadcasting in Python.mp4	17.0 MB
022. Broadcasting in Python.srt	14.3 KB
023. A note on python numpy vectors.mp4	13.0 MB
023. A note on python numpy vectors.srt	9.3 KB
024. Quick tour of Jupyter iPython Notebooks.mp4	9.7 MB
024. Quick tour of Jupyter iPython Notebooks.srt	5.9 KB
025. Explanation of logistic regression cost function (optional).mp4	11.0 MB
025. Explanation of logistic regression cost function (optional).srt	8.7 KB
/006.Heroes of Deep Learning (Optional)/
026. Pieter Abbeel interview.mp4	83.9 MB
026. Pieter Abbeel interview.srt	27.5 KB
/007.Shallow Neural Network/
027. Neural Networks Overview.mp4	7.6 MB
027. Neural Networks Overview.srt	6.8 KB
028. Neural Network Representation.mp4	8.7 MB
028. Neural Network Representation.srt	8.3 KB
029. Computing a Neural Network's Output.mp4	17.1 MB
029. Computing a Neural Network's Output.srt	16.9 KB
030. Vectorizing across multiple examples.mp4	14.5 MB
030. Vectorizing across multiple examples.srt	10.3 KB
031. Explanation for Vectorized Implementation.mp4	12.6 MB
031. Explanation for Vectorized Implementation.srt	8.9 KB
032. Activation functions.mp4	20.9 MB
032. Activation functions.srt	17.4 KB
033. Why do you need non-linear activation functions.mp4	9.7 MB
033. Why do you need non-linear activation functions.srt	7.9 KB
034. Derivatives of activation functions.mp4	11.9 MB
034. Derivatives of activation functions.srt	11.6 KB
035. Gradient descent for Neural Networks.mp4	16.8 MB
035. Gradient descent for Neural Networks.srt	13.8 KB
036. Backpropagation intuition (optional).mp4	27.3 MB
036. Backpropagation intuition (optional).srt	18.2 KB
037. Random Initialization.mp4	12.5 MB
037. Random Initialization.srt	10.6 KB
/008.Heroes of Deep Learning (Optional)/
038. Ian Goodfellow interview.mp4	57.2 MB
038. Ian Goodfellow interview.srt	23.6 KB
/009.Deep Neural Network/
039. Deep L-layer neural network.mp4	10.8 MB
039. Deep L-layer neural network.srt	7.6 KB
040. Forward Propagation in a Deep Network.mp4	13.7 MB
040. Forward Propagation in a Deep Network.srt	10.1 KB
041. Getting your matrix dimensions right.mp4	18.2 MB
041. Getting your matrix dimensions right.srt	11.7 KB
042. Why deep representations.mp4	18.4 MB
042. Why deep representations.srt	14.9 KB
043. Building blocks of deep neural networks.mp4	13.4 MB
043. Building blocks of deep neural networks.srt	11.2 KB
044. Forward and Backward Propagation.mp4	20.8 MB
044. Forward and Backward Propagation.srt	13.8 KB
045. Parameters vs Hyperparameters.mp4	10.7 MB
045. Parameters vs Hyperparameters.srt	13.3 KB
046. What does this have to do with the brain.mp4	6.3 MB
046. What does this have to do with the brain.srt	5.8 KB
/
[CourseClub.NET].url	0.1 KB
[DesireCourse.Com].url	0.1 KB
[FCS Forum].url	0.1 KB
[FreeCourseSite.com].url	0.1 KB
Total files 96