Deep Learning with TensorFlow

Deep Learning with TensorFlow

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Intro

1 of 29

1 of 29

Intro

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Classroom Contents

Deep Learning with TensorFlow

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  1. 1 Intro
  2. 2 AI and Machine Learning
  3. 3 Accuracy vs Recall
  4. 4 Programming
  5. 5 Bias
  6. 6 Linear Discriminator
  7. 7 Model
  8. 8 Output
  9. 9 H function
  10. 10 Mean squared error
  11. 11 Parabola example
  12. 12 Gradient Descent
  13. 13 Tensor Flow
  14. 14 TensorFlow Flow
  15. 15 Accuracy
  16. 16 Visual Studio
  17. 17 Training the model
  18. 18 Summary operation
  19. 19 Logs
  20. 20 Optimization Graph
  21. 21 Freezing a Model
  22. 22 Inference
  23. 23 Linear Model
  24. 24 Mathematical Induction
  25. 25 Activation Function
  26. 26 Derivatives
  27. 27 BackPropagation
  28. 28 Multilayer Perceptron
  29. 29 Atom Optimizer

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