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

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Title: MANNING Grokking Deep Learning in Motion FCOGroup: NOGRPSource: Manning
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9FC14C1572C4956A068152101F3409A6B2DE5337
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Unverified
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3.29 GB
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44
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01 - Introduction.mp490.98 MB
02 - What you need to get started.mp475.23 MB
03 - What is Deep Learning and Machine Learning.mp482.64 MB
04 - Supervised vs. unsupervised learning.mp474.87 MB
05 - Parametric vs. non-parametric learning.mp4100.17 MB
06 - Making a prediction.mp490.36 MB
07 - What does a Neural Network do.mp445.68 MB
08 - Multiple inputs.mp495.21 MB
09 - Multiple outputs and stacking predictions.mp474.21 MB
10 - Primer on NumPy.mp463.64 MB
11 - Compare and learn.mp482.56 MB
12 - Why measure error.mp458.61 MB
13 - Hot and cold learning.mp485.75 MB
14 - Gradient descent.mp480.72 MB
15 - Learning with gradient decent.mp482.45 MB
16 - The secret to learning.mp483.02 MB
17 - How to use a derivative to learn.mp4105.82 MB
18 - Alpha.mp477.21 MB
19 - Gradient descent learning with multiple inputs.mp480.31 MB
20 - Several steps of learning.mp445.96 MB
21 - Gradient descent with multiple outputs.mp439.29 MB
22 - Visualizing weight values.mp492.57 MB
23 - The streetlight problem.mp480.72 MB
24 - Building our neural network.mp496.47 MB
25 - Up and down pressure.mp4137.95 MB
26 - Correlation and backpropagation.mp492.68 MB
27 - Linear vs. non-linear.mp468 MB
28 - Our first 'deep' neural network.mp489.14 MB
29 - Simplifying.mp486.64 MB
30 - Simplified visualization.mp473.08 MB
31 - Seeing the network predict.mp480.17 MB
32 - 3-layer network on MNIST.mp4121.89 MB
33 - Overfitting in Neural Networks.mp484.43 MB
34 - Regularization - Early Stopping and Dropout.mp4185.54 MB
35 - Activation Function Constraints.mp489.94 MB
36 - Standard Activation Functions.mp4127.35 MB
37 - Softmax and implementation in code.mp4172.77 MB
38 - Where to go from here.mp473.09 MB

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