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34 Regularization - Early Stopping and Dropout.mp4
100.18MB
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02 What you need to get started.mp4
34.53MB
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03 What is Deep Learning and Machine Learning.mp4
37.06MB
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04 Supervised vs. unsupervised learning.mp4
34.56MB
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05 Parametric vs. non-parametric learning.mp4
67.48MB
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06 Making a prediction.mp4
43.31MB
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07 What does a Neural Network do.mp4
22.97MB
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08 Multiple inputs.mp4
64.47MB
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09 Multiple outputs and stacking predictions.mp4
40.91MB
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10 Primer on NumPy.mp4
35.68MB
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11 Compare and learn.mp4
39.97MB
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12 Why measure error.mp4
26.64MB
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13 Hot and cold learning.mp4
43.11MB
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14 Gradient descent.mp4
42.92MB
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15 Learning with gradient decent.mp4
51.14MB
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16 The secret to learning.mp4
41.34MB
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17 How to use a derivative to learn.mp4
76.56MB
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18 Alpha.mp4
37.05MB
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19 Gradient descent learning with multiple inputs.mp4
41.57MB
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20 Several steps of learning.mp4
26.39MB
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21 Gradient descent with multiple outputs.mp4
23.26MB
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22 Visualizing weight values.mp4
58.01MB
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23 The streetlight problem.mp4
56.72MB
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24 Building our neural network.mp4
46.76MB
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25 Up and down pressure.mp4
79.93MB
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26 Correlation and backpropagation.mp4
47.46MB
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27 Linear vs. non-linear.mp4
46.06MB
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28 Our first 'deep' neural network.mp4
45.8MB
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29 Simplifying.mp4
40.33MB
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30 Simplified visualization.mp4
46.58MB
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31 Seeing the network predict.mp4
51.24MB
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32 3-layer network on MNIST.mp4
60.89MB
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33 Overfitting in Neural Networks.mp4
38.38MB
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01 Introduction.mp4
72.69MB
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35 Activation Function Constraints.mp4
59MB
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36 Standard Activation Functions.mp4
77.03MB
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37 Softmax and implementation in code.mp4
87.91MB
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38 Where to go from here.mp4
47.54MB
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Udemy Course downloader.txt
94B
udemycoursedownloader.com.url
132B
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