Intro to Deep Learning -- L10 Input and Weight Normalization Part 1/2 [Stat453, SS20]

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Intro to Deep Learning -- L06.5 Cloud Computing [Stat453, SS20]

Sebastian Raschka

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Intro to Deep Learning -- L09 Regularization [Stat453, SS20]

Sebastian Raschka

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Intro to Deep Learning -- L10 Input and Weight Normalization Part 1/2 [Stat453, SS20]

Sebastian Raschka

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Intro to Deep Learning -- L10 Input and Weight Normalization Part 2/2 [Stat453, SS20]

Sebastian Raschka

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Intro to Deep Learning -- L11 Common Optimization Algorithms [Stat453, SS20]

Sebastian Raschka

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Intro to Deep Learning -- L12 Intro to Convolutional Neural Networks (Part 1) [Stat453, SS20]

Sebastian Raschka

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Intro to Deep Learning -- L13 Intro to Convolutional Neural Networks (Part 2) 1/2 [Stat453, SS20]

Sebastian Raschka

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Intro to Deep Learning -- L13 Intro to Convolutional Neural Networks (Part 2) 2/2 [Stat453, SS20]

Sebastian Raschka

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Intro to Deep Learning -- L14 Intro to Recurrent Neural Networks [Stat453, SS20]

Sebastian Raschka

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Intro to Deep Learning -- L15 Autoencoders [Stat453, SS20]

Sebastian Raschka

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Intro to Deep Learning -- L16 Generative Adversarial Networks [Stat453, SS20]

Sebastian Raschka

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Intro to Deep Learning -- Student Presentations, Day 1 [Stat453, SS20]

Sebastian Raschka

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1.2 What is Machine Learning (L01: What is Machine Learning)

Sebastian Raschka

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1.3 Categories of Machine Learning (L01: What is Machine Learning)

Sebastian Raschka

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1.4 Notation (L01: What is Machine Learning)

Sebastian Raschka

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1.1 Course overview (L01: What is Machine Learning)

Sebastian Raschka

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1.5 ML application (L01: What is Machine Learning)

Sebastian Raschka

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1.6 ML motivation (L01: What is Machine Learning)

Sebastian Raschka

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2.1 Introduction to NN (L02: Nearest Neighbor Methods)

Sebastian Raschka

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2.2 Nearest neighbor decision boundary (L02: Nearest Neighbor Methods)

Sebastian Raschka

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2.3 K-nearest neighbors (L02: Nearest Neighbor Methods)

Sebastian Raschka

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2.4 Big O of K-nearest neighbors (L02: Nearest Neighbor Methods)

Sebastian Raschka

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2.5 Improving k-nearest neighbors (L02: Nearest Neighbor Methods)

Sebastian Raschka

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2.6 K-nearest neighbors in Python (L02: Nearest Neighbor Methods)

Sebastian Raschka

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3.1 (Optional) Python overview

Sebastian Raschka

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3.2 (Optional) Python setup

Sebastian Raschka

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3.3 (Optional) Running Python code

Sebastian Raschka

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4.1 Intro to NumPy (L04: Scientific Computing in Python)

Sebastian Raschka

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4.2 NumPy Array Construction and Indexing (L04: Scientific Computing in Python)

Sebastian Raschka

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4.4 NumPy Broadcasting (L04: Scientific Computing in Python)

Sebastian Raschka

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4.5 NumPy Advanced Indexing -- Memory Views and Copies (L04: Scientific Computing in Python)

Sebastian Raschka

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4.3 NumPy Array Math and Universal Functions (L04: Scientific Computing in Python)

Sebastian Raschka

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4.7 Reshaping NumPy Arrays (L04: Scientific Computing in Python)

Sebastian Raschka

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4.6 NumPy Random Number Generators (L04: Scientific Computing in Python)

Sebastian Raschka

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4.8 NumPy Comparison Operators and Masks (L04: Scientific Computing in Python)

Sebastian Raschka

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4.9 NumPy Linear Algebra Basics (L04: Scientific Computing in Python)

Sebastian Raschka

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4.10 Matplotlib (L04: Scientific Computing in Python)

Sebastian Raschka

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5.1 Reading a Dataset from a Tabular Text File (L05: Machine Learning with Scikit-Learn)

Sebastian Raschka

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5.2 Basic data handling (L05: Machine Learning with Scikit-Learn)

Sebastian Raschka

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5.3 Object Oriented Programming & Python Classes (L05: Machine Learning with Scikit-Learn)

Sebastian Raschka

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5.4 Intro to Scikit-learn (L05: Machine Learning with Scikit-Learn)

Sebastian Raschka

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5.5 Scikit-learn Transformer API (L05: Machine Learning with Scikit-Learn)

Sebastian Raschka

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5.6 Scikit-learn Pipelines (L05: Machine Learning with Scikit-Learn)

Sebastian Raschka

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6.1 Intro to Decision Trees (L06: Decision Trees)

Sebastian Raschka

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6.2 Recursive algorithms & Big-O (L06: Decision Trees)

Sebastian Raschka

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6.3 Types of decision trees (L06: Decision Trees)

Sebastian Raschka

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6.5 Gini & Entropy versus misclassification error (L06: Decision Trees)

Sebastian Raschka

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6.6 Improvements & dealing with overfitting (L06: Decision Trees)

Sebastian Raschka

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6.7 Code Example Implementing Decision Trees in Scikit-Learn (L06: Decision Trees)

Sebastian Raschka

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7.1 Intro to ensemble methods (L07: Ensemble Methods)

Sebastian Raschka

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7.2 Majority Voting (L07: Ensemble Methods)

Sebastian Raschka

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7.3 Bagging (L07: Ensemble Methods)

Sebastian Raschka

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7.4 Boosting and AdaBoost (L07: Ensemble Methods)

Sebastian Raschka

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7.5 Gradient Boosting (L07: Ensemble Methods)

Sebastian Raschka

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7.6 Random Forests (L07: Ensemble Methods)

Sebastian Raschka

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7.7 Stacking (L07: Ensemble Methods)

Sebastian Raschka

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8.1 Intro to overfitting and underfitting (L08: Model Evaluation Part 1)

Sebastian Raschka

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8.2 Intuition behind bias and variance (L08: Model Evaluation Part 1)

Sebastian Raschka

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8.3 Bias-Variance Decomposition of the Squared Error (L08: Model Evaluation Part 1)

Sebastian Raschka

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8.4 Bias and Variance vs Overfitting and Underfitting (L08: Model Evaluation Part 1)

Sebastian Raschka