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Neural Networks and Deep Learning
Course Lectures
001. Welcome (5:31)
002. What is a neural network (7:16)
003. Supervised Learning with Neural Networks (8:29)
004. Why is Deep Learning taking off (10:21)
005. About this Course (2:27)
006. Course Resources (1:55)
007. Geoffrey Hinton interview (40:22)
008. Binary Classification (8:23)
009. Logistic Regression (5:59)
010. Logistic Regression Cost Function (8:11)
011. Gradient Descent (11:23)
012. Derivatives (7:10)
013. More Derivative Examples (10:27)
014. Computation graph (3:33)
015. Derivatives with a Computation Graph (14:34)
016. Logistic Regression Gradient Descent (6:42)
017. Gradient Descent on m Examples (8:00)
018. Vectorization (8:04)
019. More Vectorization Examples (6:19)
020. Vectorizing Logistic Regression (7:32)
021. Vectorizing Logistic Regression's Gradient Output (9:37)
022. Broadcasting in Python (11:06)
023. A note on python numpy vectors (6:49)
024. Quick tour of Jupyter iPython Notebooks (3:43)
025. Explanation of logistic regression cost function (optional) (7:14)
026. Pieter Abbeel interview (16:03)
027. Neural Networks Overview (4:26)
028. Neural Network Representation (5:14)
029. Computing a Neural Network's Output (9:58)
030. Vectorizing across multiple examples (9:05)
031. Explanation for Vectorized Implementation (7:37)
032. Activation functions (10:56)
033. Why do you need non-linear activation functions (5:36)
034. Derivatives of activation functions (7:57)
035. Gradient descent for Neural Networks (9:57)
036. Backpropagation intuition (optional) (15:48)
037. Random Initialization (7:57)
038. Ian Goodfellow interview (14:55)
039. Deep L-layer neural network (5:51)
040. Forward Propagation in a Deep Network (7:15)
041. Getting your matrix dimensions right (11:10)
042. Why deep representations (10:33)
043. Building blocks of deep neural networks (8:33)
044. Forward and Backward Propagation (10:29)
045. Parameters vs Hyperparameters (7:16)
046. What does this have to do with the brain (3:17)
029. Computing a Neural Network's Output
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