FreeCoursesOnline Me Coursera - Neural Networks and Deep Learning

1. 001.Welcome to the Deep Learning Specialization/001. Welcome.mp410.21MB
2. 002.Introduction to Deep Learning/002. What is a neural network.mp49.97MB
3. 002.Introduction to Deep Learning/003. Supervised Learning with Neural Networks.mp412.9MB
4. 002.Introduction to Deep Learning/004. Why is Deep Learning taking off.mp418.64MB
5. 002.Introduction to Deep Learning/005. About this Course.mp44.66MB
6. 002.Introduction to Deep Learning/006. Course Resources.mp42.5MB
7. 003.Heroes of Deep Learning (Optional)/007. Geoffrey Hinton interview.mp4191.76MB
8. 004.Logistic Regression as a Neural Network/008. Binary Classification.mp415.24MB
9. 004.Logistic Regression as a Neural Network/009. Logistic Regression.mp48.48MB
10. 004.Logistic Regression as a Neural Network/010. Logistic Regression Cost Function.mp413.19MB
11. 004.Logistic Regression as a Neural Network/011. Gradient Descent.mp417.05MB
12. 004.Logistic Regression as a Neural Network/012. Derivatives.mp413.41MB
13. 004.Logistic Regression as a Neural Network/013. More Derivative Examples.mp416.76MB
14. 004.Logistic Regression as a Neural Network/014. Computation graph.mp45.66MB
15. 004.Logistic Regression as a Neural Network/015. Derivatives with a Computation Graph.mp421.69MB
16. 004.Logistic Regression as a Neural Network/016. Logistic Regression Gradient Descent.mp411.15MB
17. 004.Logistic Regression as a Neural Network/017. Gradient Descent on m Examples.mp412.17MB
18. 005.Python and Vectorization/018. Vectorization.mp412.6MB
19. 005.Python and Vectorization/019. More Vectorization Examples.mp410.34MB
20. 005.Python and Vectorization/020. Vectorizing Logistic Regression.mp411.46MB
21. 005.Python and Vectorization/021. Vectorizing Logistic Regression's Gradient Output.mp415.55MB
22. 005.Python and Vectorization/022. Broadcasting in Python.mp416.17MB
23. 005.Python and Vectorization/023. A note on python numpy vectors.mp412.36MB
24. 005.Python and Vectorization/024. Quick tour of Jupyter iPython Notebooks.mp49.23MB
25. 005.Python and Vectorization/025. Explanation of logistic regression cost function (optional).mp410.47MB
26. 006.Heroes of Deep Learning (Optional)/026. Pieter Abbeel interview.mp480.04MB
27. 007.Shallow Neural Network/027. Neural Networks Overview.mp47.23MB
28. 007.Shallow Neural Network/028. Neural Network Representation.mp48.26MB
29. 007.Shallow Neural Network/029. Computing a Neural Network's Output.mp416.32MB
30. 007.Shallow Neural Network/030. Vectorizing across multiple examples.mp413.86MB
31. 007.Shallow Neural Network/031. Explanation for Vectorized Implementation.mp411.97MB
32. 007.Shallow Neural Network/032. Activation functions.mp419.93MB
33. 007.Shallow Neural Network/033. Why do you need non-linear activation functions.mp49.29MB
34. 007.Shallow Neural Network/034. Derivatives of activation functions.mp411.38MB
35. 007.Shallow Neural Network/035. Gradient descent for Neural Networks.mp416.01MB
36. 007.Shallow Neural Network/036. Backpropagation intuition (optional).mp426.04MB
37. 007.Shallow Neural Network/037. Random Initialization.mp411.96MB
38. 008.Heroes of Deep Learning (Optional)/038. Ian Goodfellow interview.mp454.53MB
39. 009.Deep Neural Network/039. Deep L-layer neural network.mp410.35MB
40. 009.Deep Neural Network/040. Forward Propagation in a Deep Network.mp413.02MB
41. 009.Deep Neural Network/041. Getting your matrix dimensions right.mp417.35MB
42. 009.Deep Neural Network/042. Why deep representations.mp417.59MB
43. 009.Deep Neural Network/043. Building blocks of deep neural networks.mp412.81MB
44. 009.Deep Neural Network/044. Forward and Backward Propagation.mp419.8MB
45. 009.Deep Neural Network/045. Parameters vs Hyperparameters.mp410.21MB
46. 009.Deep Neural Network/046. What does this have to do with the brain.mp46MB