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| 01 - Introduction.mp4 | 90.98 MB |
| 02 - What you need to get started.mp4 | 75.23 MB |
| 03 - What is Deep Learning and Machine Learning.mp4 | 82.64 MB |
| 04 - Supervised vs. unsupervised learning.mp4 | 74.87 MB |
| 05 - Parametric vs. non-parametric learning.mp4 | 100.17 MB |
| 06 - Making a prediction.mp4 | 90.36 MB |
| 07 - What does a Neural Network do.mp4 | 45.68 MB |
| 08 - Multiple inputs.mp4 | 95.21 MB |
| 09 - Multiple outputs and stacking predictions.mp4 | 74.21 MB |
| 10 - Primer on NumPy.mp4 | 63.64 MB |
| 11 - Compare and learn.mp4 | 82.56 MB |
| 12 - Why measure error.mp4 | 58.61 MB |
| 13 - Hot and cold learning.mp4 | 85.75 MB |
| 14 - Gradient descent.mp4 | 80.72 MB |
| 15 - Learning with gradient decent.mp4 | 82.45 MB |
| 16 - The secret to learning.mp4 | 83.02 MB |
| 17 - How to use a derivative to learn.mp4 | 105.82 MB |
| 18 - Alpha.mp4 | 77.21 MB |
| 19 - Gradient descent learning with multiple inputs.mp4 | 80.31 MB |
| 20 - Several steps of learning.mp4 | 45.96 MB |
| 21 - Gradient descent with multiple outputs.mp4 | 39.29 MB |
| 22 - Visualizing weight values.mp4 | 92.57 MB |
| 23 - The streetlight problem.mp4 | 80.72 MB |
| 24 - Building our neural network.mp4 | 96.47 MB |
| 25 - Up and down pressure.mp4 | 137.95 MB |
| 26 - Correlation and backpropagation.mp4 | 92.68 MB |
| 27 - Linear vs. non-linear.mp4 | 68.00 MB |
| 28 - Our first 'deep' neural network.mp4 | 89.14 MB |
| 29 - Simplifying.mp4 | 86.64 MB |
| 30 - Simplified visualization.mp4 | 73.08 MB |
| 31 - Seeing the network predict.mp4 | 80.17 MB |
| 32 - 3-layer network on MNIST.mp4 | 121.89 MB |
| 33 - Overfitting in Neural Networks.mp4 | 84.43 MB |
| 34 - Regularization - Early Stopping and Dropout.mp4 | 185.54 MB |
| 35 - Activation Function Constraints.mp4 | 89.94 MB |
| 36 - Standard Activation Functions.mp4 | 127.35 MB |
| 37 - Softmax and implementation in code.mp4 | 172.77 MB |
| 38 - Where to go from here.mp4 | 73.09 MB |
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