[UdemyCourseDownloader] Grokking Deep Learning in Motion
File List
- 34 Regularization - Early Stopping and Dropout.mp4 100.2 MB
- 37 Softmax and implementation in code.mp4 87.9 MB
- 25 Up and down pressure.mp4 79.9 MB
- 36 Standard Activation Functions.mp4 77.0 MB
- 17 How to use a derivative to learn.mp4 76.6 MB
- 01 Introduction.mp4 72.7 MB
- 05 Parametric vs. non-parametric learning.mp4 67.5 MB
- 08 Multiple inputs.mp4 64.5 MB
- 32 3-layer network on MNIST.mp4 60.9 MB
- 35 Activation Function Constraints.mp4 59.0 MB
- 22 Visualizing weight values.mp4 58.0 MB
- 23 The streetlight problem.mp4 56.7 MB
- 31 Seeing the network predict.mp4 51.2 MB
- 15 Learning with gradient decent.mp4 51.1 MB
- 38 Where to go from here.mp4 47.5 MB
- 26 Correlation and backpropagation.mp4 47.5 MB
- 24 Building our neural network.mp4 46.8 MB
- 30 Simplified visualization.mp4 46.6 MB
- 27 Linear vs. non-linear.mp4 46.1 MB
- 28 Our first 'deep' neural network.mp4 45.8 MB
- 06 Making a prediction.mp4 43.3 MB
- 13 Hot and cold learning.mp4 43.1 MB
- 14 Gradient descent.mp4 42.9 MB
- 19 Gradient descent learning with multiple inputs.mp4 41.6 MB
- 16 The secret to learning.mp4 41.3 MB
- 09 Multiple outputs and stacking predictions.mp4 40.9 MB
- 29 Simplifying.mp4 40.3 MB
- 11 Compare and learn.mp4 40.0 MB
- 33 Overfitting in Neural Networks.mp4 38.4 MB
- 03 What is Deep Learning and Machine Learning.mp4 37.1 MB
- 18 Alpha.mp4 37.0 MB
- 10 Primer on NumPy.mp4 35.7 MB
- 04 Supervised vs. unsupervised learning.mp4 34.6 MB
- 02 What you need to get started.mp4 34.5 MB
- 12 Why measure error.mp4 26.6 MB
- 20 Several steps of learning.mp4 26.4 MB
- 21 Gradient descent with multiple outputs.mp4 23.3 MB
- 07 What does a Neural Network do.mp4 23.0 MB
- udemycoursedownloader.com.url 132 bytes
- Udemy Course downloader.txt 94 bytes
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