La Source, Act 2, Introduction

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Model Representation II 6m.

La source (Saint-Léon) - WikiVisually

Examples and Intuitions I 2m. Examples and Intuitions II 3m. Neural Networks: Representation 10m. Show More. Cost Function 6m. Backpropagation Algorithm 11m. Backpropagation Intuition 12m. Implementation Note: Unrolling Parameters 7m. Gradient Checking 11m. Random Initialization 6m.

Cast and vocal parts

Putting It Together 13m. Autonomous Driving 6m. Cost Function 4m. Backpropagation Algorithm 10m. Backpropagation Intuition 4m. Implementation Note: Unrolling Parameters 3m. Gradient Checking 3m.

Random Initialization 3m. Putting It Together 4m. Neural Networks: Learning 10m. Deciding What to Try Next 5m. Evaluating a Hypothesis 7m. Diagnosing Bias vs. Variance 7m. Learning Curves 11m. Deciding What to Do Next Revisited 6m. Evaluating a Hypothesis 4m. Variance 3m. Learning Curves 3m. Deciding What to do Next Revisited 3m. Advice for Applying Machine Learning 10m.

Key Issues

Prioritizing What to Work On 9m. Error Analysis 13m. Error Metrics for Skewed Classes 11m. Trading Off Precision and Recall 14m. Data For Machine Learning 11m. Reading 3 readings. Prioritizing What to Work On 3m. Error Analysis 3m. Machine Learning System Design 10m.

Optimization Objective 14m. Large Margin Intuition 10m. Mathematics Behind Large Margin Classification 19m. Kernels I 15m. Kernels II 15m. Using An SVM 21m. Support Vector Machines 10m. Unsupervised Learning: Introduction 3m. K-Means Algorithm 12m. Optimization Objective 7m. Random Initialization 7m. Choosing the Number of Clusters 8m.

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Unsupervised Learning 10m. Motivation I: Data Compression 10m. Motivation II: Visualization 5m. Principal Component Analysis Problem Formulation 9m. Principal Component Analysis Algorithm 15m. Reconstruction from Compressed Representation 3m. Choosing the Number of Principal Components 10m. Advice for Applying PCA 12m. Principal Component Analysis 10m. Problem Motivation 7m. Gaussian Distribution 10m. Algorithm 12m.

Developing and Evaluating an Anomaly Detection System 13m. Anomaly Detection vs. Supervised Learning 7m. Choosing What Features to Use 12m. Multivariate Gaussian Distribution 13m. Anomaly Detection using the Multivariate Gaussian Distribution 14m. Anomaly Detection 10m. Problem Formulation 7m. Content Based Recommendations 14m.

Collaborative Filtering 10m. Collaborative Filtering Algorithm 8m. Vectorization: Low Rank Matrix Factorization 8m. Implementational Detail: Mean Normalization 8m. Recommender Systems 10m. Learning With Large Datasets 5m. Stochastic Gradient Descent 13m. Mini-Batch Gradient Descent 6m.

La source, ou Naïla (ballet; in collaboration with Léon Minkus)

Stochastic Gradient Descent Convergence 11m. Online Learning 12m. Map Reduce and Data Parallelism 14m. Large Scale Machine Learning 10m. Problem Description and Pipeline 7m. Sliding Windows 14m.


  1. Information Menu.
  2. Introduction;
  3. Linear Regression with One Variable.

Getting Lots of Data and Artificial Data 16m. Summary and Thank You 4m. Selecting one will carry over the choices made in that saved game to The Witcher 3. Note that the list of saves may not necessarily be in chronological order, therefore, care must be taken to ensure that the correct save is selected, so that all the desired choices are carried over.