Ultimate Neural Network and Deep Learning Masterclasss

John Harper, Cambridge Programmer, AI engineer

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42 Lessons (4h 55m)
    • 1. Promo vid

      1:42
    • 2. About the class

      1:59
    • 3. How to maximise your learning

      4:03
    • 4. What is deep learning

      11:03
    • 5. Real world applications

      13:01
    • 6. Linear regression

      7:44
    • 7. Line of best fit

      10:20
    • 8. Linear regression with big data

      5:09
    • 9. Overfitting

      6:47
    • 10. Cost and loss

      9:34
    • 11. How do NNs learn

      5:08
    • 12. Neural networks recap

      20:27
    • 13. Training wheels off neural networks

      13:33
    • 14. Adding an activation function

      5:43
    • 15. First neural network

      15:28
    • 16. Multiple inputs

      5:59
    • 17. Hidden layers

      4:37
    • 18. Back propagation

      12:41
    • 19. Installing python

      5:25
    • 20. Jupyter notebook

      6:28
    • 21. Command line needs text for linux and mac commands

      4:41
    • 22. AWS

      3:10
    • 23. Hello world tensorflow

      6:02
    • 24. Feeding data and running sessions

      8:06
    • 25. Data structures

      3:31
    • 26. Loading data into tensorflow

      12:14
    • 27. Lloading data part 2

      7:20
    • 28. One hot coding

      2:42
    • 29. Neural network lets go

      8:17
    • 30. Give your network a brain

      14:57
    • 31. Running your model

      8:48
    • 32. Using other frameworkers

      3:25
    • 33. Loading handwritten digits

      7:39
    • 34. Creating the model

      4:45
    • 35. Running your model

      9:50
    • 36. Hyperparameters

      3:35
    • 37. Bias and variance

      4:50
    • 38. Strategies for reducing bias and variance

      6:34
    • 39. Activation functions

      1:55
    • 40. What we have covered

      3:11
    • 41. How to continue progressing

      1:55
    • 42. Thank you

      1:03