[FreeCoursesOnline.Me] [MANNING] Grokking Deep Learning in Motion [FCO]

34 - Regularization - Early Stopping and Dropout.mp4

37 - Softmax and implementation in code.mp4

25 - Up and down pressure.mp4

36 - Standard Activation Functions.mp4

32 - 3-layer network on MNIST.mp4

17 - How to use a derivative to learn.mp4

05 - Parametric vs. non-parametric learning.mp4

24 - Building our neural network.mp4

08 - Multiple inputs.mp4

26 - Correlation and backpropagation.mp4

22 - Visualizing weight values.mp4

01 - Introduction.mp4

06 - Making a prediction.mp4

35 - Activation Function Constraints.mp4

28 - Our first 'deep' neural network.mp4

29 - Simplifying.mp4

13 - Hot and cold learning.mp4

33 - Overfitting in Neural Networks.mp4

16 - The secret to learning.mp4

03 - What is Deep Learning and Machine Learning.mp4

11 - Compare and learn.mp4

15 - Learning with gradient decent.mp4

23 - The streetlight problem.mp4

14 - Gradient descent.mp4

19 - Gradient descent learning with multiple inputs.mp4

31 - Seeing the network predict.mp4

18 - Alpha.mp4

02 - What you need to get started.mp4

04 - Supervised vs. unsupervised learning.mp4

09 - Multiple outputs and stacking predictions.mp4

38 - Where to go from here.mp4

30 - Simplified visualization.mp4

27 - Linear vs. non-linear.mp4

10 - Primer on NumPy.mp4

12 - Why measure error.mp4

20 - Several steps of learning.mp4

07 - What does a Neural Network do.mp4

21 - Gradient descent with multiple outputs.mp4

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