L11.6 Xavier Glorot and Kaiming He Initialization

Sebastian Raschka · Beginner ·📐 ML Fundamentals ·5y ago
Sebastian's books: https://sebastianraschka.com/books/I MPORTANT NOTE: In the video, I talk about the number of input units in the denominator ("fan in"), but to be correct, it should have been number of input units for both the current and the next layer ("fan in" + "fan out"). Slides: https://sebastianraschka.com/pdf/lecture-notes/stat453ss21/L11_norm-and-init__slides.pdf Papers: Xavier Glorot and Yoshua Bengio. "Understanding the difficulty of training deep feedforward neural networks." Proceedings of the thirteenth international conference on artificial intelligence and statistics. 201…
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1 Sebastian Raschka - SIteInterlock
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 2 Intro to Deep Learning -- L06.5 Cloud Computing [Stat453, SS20]
Intro to Deep Learning -- L06.5 Cloud Computing [Stat453, SS20]
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 3 Intro to Deep Learning -- L09 Regularization [Stat453, SS20]
Intro to Deep Learning -- L09 Regularization [Stat453, SS20]
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 4 Intro to Deep Learning -- L10 Input and Weight Normalization Part 1/2 [Stat453, SS20]
Intro to Deep Learning -- L10 Input and Weight Normalization Part 1/2 [Stat453, SS20]
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 5 Intro to Deep Learning -- L10 Input and Weight Normalization Part 2/2 [Stat453, SS20]
Intro to Deep Learning -- L10 Input and Weight Normalization Part 2/2 [Stat453, SS20]
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 6 Intro to Deep Learning -- L11 Common Optimization Algorithms [Stat453, SS20]
Intro to Deep Learning -- L11 Common Optimization Algorithms [Stat453, SS20]
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 7 Intro to Deep Learning -- L12 Intro to Convolutional Neural Networks (Part 1) [Stat453, SS20]
Intro to Deep Learning -- L12 Intro to Convolutional Neural Networks (Part 1) [Stat453, SS20]
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 8 Intro to Deep Learning -- L13 Intro to Convolutional Neural Networks (Part 2) 1/2 [Stat453, SS20]
Intro to Deep Learning -- L13 Intro to Convolutional Neural Networks (Part 2) 1/2 [Stat453, SS20]
Sebastian Raschka
 9 Intro to Deep Learning -- L13 Intro to Convolutional Neural Networks (Part 2) 2/2 [Stat453, SS20]
Intro to Deep Learning -- L13 Intro to Convolutional Neural Networks (Part 2) 2/2 [Stat453, SS20]
Sebastian Raschka
 10 Intro to Deep Learning -- L14 Intro to Recurrent Neural Networks [Stat453, SS20]
Intro to Deep Learning -- L14 Intro to Recurrent Neural Networks [Stat453, SS20]
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 11 Intro to Deep Learning -- L15 Autoencoders [Stat453, SS20]
Intro to Deep Learning -- L15 Autoencoders [Stat453, SS20]
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 12 Intro to Deep Learning -- L16 Generative Adversarial Networks [Stat453, SS20]
Intro to Deep Learning -- L16 Generative Adversarial Networks [Stat453, SS20]
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 13 Intro to Deep Learning -- Student Presentations, Day 1 [Stat453, SS20]
Intro to Deep Learning -- Student Presentations, Day 1 [Stat453, SS20]
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 14 1.2 What is Machine Learning (L01: What is Machine Learning)
1.2 What is Machine Learning (L01: What is Machine Learning)
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 15 1.3 Categories of Machine Learning (L01: What is Machine Learning)
1.3 Categories of Machine Learning (L01: What is Machine Learning)
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 16 1.4 Notation (L01: What is Machine Learning)
1.4 Notation (L01: What is Machine Learning)
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 17 1.1 Course overview (L01: What is Machine Learning)
1.1 Course overview (L01: What is Machine Learning)
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 18 1.5 ML application (L01: What is Machine Learning)
1.5 ML application (L01: What is Machine Learning)
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 19 1.6 ML motivation (L01: What is Machine Learning)
1.6 ML motivation (L01: What is Machine Learning)
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 20 2.1 Introduction to NN (L02: Nearest Neighbor Methods)
2.1 Introduction to NN (L02: Nearest Neighbor Methods)
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 21 2.2 Nearest neighbor decision boundary (L02: Nearest Neighbor Methods)
2.2 Nearest neighbor decision boundary (L02: Nearest Neighbor Methods)
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 22 2.3 K-nearest neighbors (L02: Nearest Neighbor Methods)
2.3 K-nearest neighbors (L02: Nearest Neighbor Methods)
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 23 2.4 Big O of K-nearest neighbors (L02: Nearest Neighbor Methods)
2.4 Big O of K-nearest neighbors (L02: Nearest Neighbor Methods)
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 24 2.5 Improving k-nearest neighbors (L02: Nearest Neighbor Methods)
2.5 Improving k-nearest neighbors (L02: Nearest Neighbor Methods)
Sebastian Raschka
 25 2.6 K-nearest neighbors in Python (L02: Nearest Neighbor Methods)
2.6 K-nearest neighbors in Python (L02: Nearest Neighbor Methods)
Sebastian Raschka
 26 3.1 (Optional) Python overview
3.1 (Optional) Python overview
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 27 3.2 (Optional) Python setup
3.2 (Optional) Python setup
Sebastian Raschka
 28 3.3 (Optional) Running Python code
3.3 (Optional) Running Python code
Sebastian Raschka
 29 4.1 Intro to NumPy (L04: Scientific Computing in Python)
4.1 Intro to NumPy (L04: Scientific Computing in Python)
Sebastian Raschka
 30 4.2 NumPy Array Construction and Indexing (L04: Scientific Computing in Python)
4.2 NumPy Array Construction and Indexing (L04: Scientific Computing in Python)
Sebastian Raschka
 31 4.4 NumPy Broadcasting (L04: Scientific Computing in Python)
4.4 NumPy Broadcasting (L04: Scientific Computing in Python)
Sebastian Raschka
 32 4.5 NumPy Advanced Indexing -- Memory Views and Copies (L04: Scientific Computing in Python)
4.5 NumPy Advanced Indexing -- Memory Views and Copies (L04: Scientific Computing in Python)
Sebastian Raschka
 33 4.3 NumPy Array Math and Universal Functions (L04: Scientific Computing in Python)
4.3 NumPy Array Math and Universal Functions (L04: Scientific Computing in Python)
Sebastian Raschka
 34 4.7 Reshaping NumPy Arrays (L04: Scientific Computing in Python)
4.7 Reshaping NumPy Arrays (L04: Scientific Computing in Python)
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 35 4.6 NumPy Random Number Generators (L04: Scientific Computing in Python)
4.6 NumPy Random Number Generators (L04: Scientific Computing in Python)
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 36 4.8 NumPy Comparison Operators and Masks (L04: Scientific Computing in Python)
4.8 NumPy Comparison Operators and Masks (L04: Scientific Computing in Python)
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 37 4.9 NumPy Linear Algebra Basics (L04: Scientific Computing in Python)
4.9 NumPy Linear Algebra Basics (L04: Scientific Computing in Python)
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 38 4.10 Matplotlib (L04: Scientific Computing in Python)
4.10 Matplotlib (L04: Scientific Computing in Python)
Sebastian Raschka
 39 5.1 Reading a Dataset from a Tabular Text File (L05: Machine Learning with Scikit-Learn)
5.1 Reading a Dataset from a Tabular Text File (L05: Machine Learning with Scikit-Learn)
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 40 5.2 Basic data handling (L05: Machine Learning with Scikit-Learn)
5.2 Basic data handling (L05: Machine Learning with Scikit-Learn)
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 41 5.3 Object Oriented Programming & Python Classes (L05: Machine Learning with Scikit-Learn)
5.3 Object Oriented Programming & Python Classes (L05: Machine Learning with Scikit-Learn)
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 42 5.4 Intro to Scikit-learn (L05: Machine Learning with Scikit-Learn)
5.4 Intro to Scikit-learn (L05: Machine Learning with Scikit-Learn)
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 43 5.5 Scikit-learn Transformer API (L05: Machine Learning with Scikit-Learn)
5.5 Scikit-learn Transformer API (L05: Machine Learning with Scikit-Learn)
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 44 5.6 Scikit-learn Pipelines (L05: Machine Learning with Scikit-Learn)
5.6 Scikit-learn Pipelines (L05: Machine Learning with Scikit-Learn)
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 45 6.1 Intro to Decision Trees (L06: Decision Trees)
6.1 Intro to Decision Trees (L06: Decision Trees)
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 46 6.2 Recursive algorithms & Big-O (L06: Decision Trees)
6.2 Recursive algorithms & Big-O (L06: Decision Trees)
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 47 6.3 Types of decision trees (L06: Decision Trees)
6.3 Types of decision trees (L06: Decision Trees)
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 48 6.4 Splitting criteria (L06: Decision Trees)
6.4 Splitting criteria (L06: Decision Trees)
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 49 About the Midterm exam
About the Midterm exam
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 50 6.5 Gini & Entropy versus misclassification error (L06: Decision Trees)
6.5 Gini & Entropy versus misclassification error (L06: Decision Trees)
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 51 6.6 Improvements & dealing with overfitting (L06: Decision Trees)
6.6 Improvements & dealing with overfitting (L06: Decision Trees)
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 52 6.7 Code Example Implementing Decision Trees in Scikit-Learn (L06: Decision Trees)
6.7 Code Example Implementing Decision Trees in Scikit-Learn (L06: Decision Trees)
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 53 7.1 Intro to ensemble methods (L07: Ensemble Methods)
7.1 Intro to ensemble methods (L07: Ensemble Methods)
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 54 7.2 Majority Voting (L07: Ensemble Methods)
7.2 Majority Voting (L07: Ensemble Methods)
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 55 7.3 Bagging (L07: Ensemble Methods)
7.3 Bagging (L07: Ensemble Methods)
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 56 7.4 Boosting and AdaBoost (L07: Ensemble Methods)
7.4 Boosting and AdaBoost (L07: Ensemble Methods)
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 57 7.5 Gradient Boosting (L07: Ensemble Methods)
7.5 Gradient Boosting (L07: Ensemble Methods)
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 58 7.6 Random Forests (L07: Ensemble Methods)
7.6 Random Forests (L07: Ensemble Methods)
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 59 7.7 Stacking (L07: Ensemble Methods)
7.7 Stacking (L07: Ensemble Methods)
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 60 8.1 Intro to overfitting and underfitting (L08: Model Evaluation Part 1)
8.1 Intro to overfitting and underfitting (L08: Model Evaluation Part 1)
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