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Multi Output Perceptron
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Classroom Contents
Introduction to Deep Learning - MIT 2018
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- 1 Intro
- 2 What is Deep Learning
- 3 Deep Learning Success: Vision
- 4 Deep Learning Success: Audio
- 5 Administrative Information
- 6 Final Class Project
- 7 Class Support
- 8 Course Staff
- 9 Why Deep Learning
- 10 The Perceptron: Forward Propagation
- 11 Common Activation Functions
- 12 Importance of Activation Functions
- 13 The Perceptron: Example
- 14 The Perceptron: Simplified
- 15 Multi Output Perceptron
- 16 Single Layer Neural Network
- 17 Deep Neural Network
- 18 Quantifying Loss
- 19 Empirical Loss
- 20 Binary Cross Entropy Loss
- 21 Mean Squared Error Loss
- 22 Loss Optimization
- 23 Computing Gradients: Backpropagation
- 24 Training Neural Networks is Difficult
- 25 Setting the Learning Rate
- 26 Adaptive Learning Rates
- 27 Adaptive Learning Rate Algorithms
- 28 Stochastic Gradient Descent
- 29 The Problem of Overfitting
- 30 Regularization 2: Early Stopping
- 31 Core Foundation Review
