The document discusses hyperparameter optimization in machine learning models. It introduces various hyperparameters that can affect model performance, and notes that as models become more complex, the number of hyperparameters increases, making manual tuning difficult. It formulates hyperparameter optimization as a black-box optimization problem to minimize validation loss and discusses challenges like high function evaluation costs and lack of gradient information.
The document discusses hyperparameter optimization in machine learning models. It introduces various hyperparameters that can affect model performance, and notes that as models become more complex, the number of hyperparameters increases, making manual tuning difficult. It formulates hyperparameter optimization as a black-box optimization problem to minimize validation loss and discusses challenges like high function evaluation costs and lack of gradient information.
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.
This document summarizes recent developments in action recognition using deep learning techniques. It discusses early approaches using improved dense trajectories and two-stream convolutional neural networks. It then focuses on advances using 3D convolutional networks, enabled by large video datasets like Kinetics. State-of-the-art results are achieved using inflated 3D convolutional networks and temporal aggregation methods like temporal linear encoding. The document provides an overview of popular datasets and challenges and concludes with tips on training models at scale.
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.
This document summarizes recent developments in action recognition using deep learning techniques. It discusses early approaches using improved dense trajectories and two-stream convolutional neural networks. It then focuses on advances using 3D convolutional networks, enabled by large video datasets like Kinetics. State-of-the-art results are achieved using inflated 3D convolutional networks and temporal aggregation methods like temporal linear encoding. The document provides an overview of popular datasets and challenges and concludes with tips on training models at scale.