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01 - Introduction.mp4
90.98MB
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02 - What you need to get started.mp4
75.23MB
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03 - What is Deep Learning and Machine Learning.mp4
82.64MB
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04 - Supervised vs. unsupervised learning.mp4
74.87MB
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05 - Parametric vs. non-parametric learning.mp4
100.17MB
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06 - Making a prediction.mp4
90.36MB
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07 - What does a Neural Network do.mp4
45.68MB
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08 - Multiple inputs.mp4
95.21MB
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09 - Multiple outputs and stacking predictions.mp4
74.21MB
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10 - Primer on NumPy.mp4
63.64MB
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11 - Compare and learn.mp4
82.56MB
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12 - Why measure error.mp4
58.61MB
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13 - Hot and cold learning.mp4
85.75MB
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14 - Gradient descent.mp4
80.72MB
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15 - Learning with gradient decent.mp4
82.45MB
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16 - The secret to learning.mp4
83.02MB
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17 - How to use a derivative to learn.mp4
105.82MB
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18 - Alpha.mp4
77.21MB
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19 - Gradient descent learning with multiple inputs.mp4
80.31MB
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20 - Several steps of learning.mp4
45.96MB
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21 - Gradient descent with multiple outputs.mp4
39.29MB
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22 - Visualizing weight values.mp4
92.57MB
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23 - The streetlight problem.mp4
80.72MB
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24 - Building our neural network.mp4
96.47MB
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25 - Up and down pressure.mp4
137.95MB
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26 - Correlation and backpropagation.mp4
92.68MB
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27 - Linear vs. non-linear.mp4
68MB
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28 - Our first 'deep' neural network.mp4
89.14MB
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29 - Simplifying.mp4
86.64MB
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30 - Simplified visualization.mp4
73.08MB
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31 - Seeing the network predict.mp4
80.17MB
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32 - 3-layer network on MNIST.mp4
121.89MB
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33 - Overfitting in Neural Networks.mp4
84.43MB
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34 - Regularization - Early Stopping and Dropout.mp4
185.54MB
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35 - Activation Function Constraints.mp4
89.94MB
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36 - Standard Activation Functions.mp4
127.35MB
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37 - Softmax and implementation in code.mp4
172.77MB
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38 - Where to go from here.mp4
73.09MB
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