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Grokking Deep Learning in Motion

INTRODUCING DEEP LEARNING
Introduction 00:08:54
What you need to get started 00:05:28
FUNDAMENTAL CONCEPTS
What is Deep Learning and Machine Learning? 00:05:02
Supervised vs. unsupervised learning 00:05:23
Parametric vs. non-parametric learning 00:12:56
INTRODUCTION TO NEURAL PREDICTION
Making a prediction 00:07:56
What does a Neural Network do? 00:04:05
Multiple inputs 00:13:25
Multiple outputs and stacking predictions 00:09:15
Primer on NumPy 00:11:27
INTRODUCTION TO NEURAL LEARNING
Compare and learn 00:06:23
Why measure error? 00:03:53
Hot and cold learning 00:09:17
Gradient descent 00:09:21
Learning with gradient decent 00:09:06
The secret to learning 00:07:13
How to use a derivative to learn 00:11:41
Alpha 00:06:13
LEARNING MULTIPLE WEIGHTS AT A TIME
Gradient descent learning with multiple inputs 00:07:16
Several steps of learning 00:06:04
Gradient descent with multiple outputs 00:06:17
Visualizing weight values 00:09:32
BUILDING YOUR FIRST "DEEP" NEURAL NETWORK
The streetlight problem 00:10:32
Building our neural network 00:09:37
Up and down pressure 00:14:41
Correlation and backpropagation 00:08:04
Linear vs. non-linear 00:08:06
Our first "deep" neural network 00:10:14
HOW TO PICTURE NEURAL NETWORKS
Simplifying 00:06:35
Simplified visualization 00:07:16
Seeing the network predict 00:08:04
LEARNING SIGNAL AND IGNORING NOISE
3-layer network on MNIST 00:10:59
Overfitting in Neural Networks 00:06:06
Regularization: Early Stopping and Dropout 00:16:45
MODELING PROBABILITIES AND NON-LINEARITIES
Activation Function Constraints 00:09:31
Standard Activation Functions 00:12:22
Softmax and implementation in code 00:16:35
CONCLUSION
Where to go from here 00:07:17

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