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