The document discusses distances between data and similarity measures in data analysis. It introduces the concept of distance between data as a quantitative measure of how different two data points are, with smaller distances indicating greater similarity. Distances are useful for tasks like clustering data, detecting anomalies, data recognition, and measuring approximation errors. The most common distance measure, Euclidean distance, is explained for vectors of any dimension using the concept of norm from geometry. Caution is advised when calculating distances between data with differing scales.
The document discusses distances between data and similarity measures in data analysis. It introduces the concept of distance between data as a quantitative measure of how different two data points are, with smaller distances indicating greater similarity. Distances are useful for tasks like clustering data, detecting anomalies, data recognition, and measuring approximation errors. The most common distance measure, Euclidean distance, is explained for vectors of any dimension using the concept of norm from geometry. Caution is advised when calculating distances between data with differing scales.
- The document discusses linear regression models and methods for estimating coefficients, including ordinary least squares and regularization methods like ridge regression and lasso regression.
- It explains how lasso regression, unlike ordinary least squares and ridge regression, has the property of driving some of the coefficient estimates exactly to zero, allowing for variable selection.
- An example using crime rate data shows how lasso regression can select a more parsimonious model than other methods by setting some coefficients to zero.
1) The document discusses recent advances in deep reinforcement learning algorithms for continuous control tasks. It examines factors like network architecture, reward scaling, random seeds, environments and codebases that impact reproducibility of deep RL results.
2) It analyzes the performance of algorithms like ACKTR, PPO, DDPG and TRPO on benchmarks like Hopper, HalfCheetah and identifies unstable behaviors and unfair comparisons.
3) Simpler approaches like nearest neighbor policies are explored as alternatives to deep networks for solving continuous control tasks, especially in sparse reward settings.
- The document discusses linear regression models and methods for estimating coefficients, including ordinary least squares and regularization methods like ridge regression and lasso regression.
- It explains how lasso regression, unlike ordinary least squares and ridge regression, has the property of driving some of the coefficient estimates exactly to zero, allowing for variable selection.
- An example using crime rate data shows how lasso regression can select a more parsimonious model than other methods by setting some coefficients to zero.
1) The document discusses recent advances in deep reinforcement learning algorithms for continuous control tasks. It examines factors like network architecture, reward scaling, random seeds, environments and codebases that impact reproducibility of deep RL results.
2) It analyzes the performance of algorithms like ACKTR, PPO, DDPG and TRPO on benchmarks like Hopper, HalfCheetah and identifies unstable behaviors and unfair comparisons.
3) Simpler approaches like nearest neighbor policies are explored as alternatives to deep networks for solving continuous control tasks, especially in sparse reward settings.
Math in Machine Learning / PCA and SVD with ApplicationsKenji Hiranabe
Math in Machine Learning / PCA and SVD with Applications
機会学習の数学とPCA/SVD
Colab での練習コードつきです.コードはこちら.
https://colab.research.google.com/drive/1YZgZWX5a7_MGA__HV2bybSuJsqkd4XxD?usp=sharing