This document summarizes a presentation on the bandit problem and algorithms to solve it. The presentation will:
1) Explain what the bandit problem is and provide a simple example.
2) Describe algorithms for solving the bandit problem, including epsilon-greedy and Thompson sampling.
3) Discuss how to apply bandit algorithms to problems that include contextual information.
This document summarizes a presentation on the bandit problem and algorithms to solve it. The presentation will:
1) Explain what the bandit problem is and provide a simple example.
2) Describe algorithms for solving the bandit problem, including epsilon-greedy and Thompson sampling.
3) Discuss how to apply bandit algorithms to problems that include contextual information.
I introduced some key concepts in linear regression models:
1. Linear regression aims to fit a linear function to data to minimize error.
2. Maximum likelihood estimation is equivalent to least squares regression.
3. MAP estimation with a Gaussian prior is equivalent to ridge regression.
4. Linear classification models predict class probabilities using multiple linear functions.
5. The least squares method for classification has disadvantages like being sensitive to outliers.
I introduced some key concepts in linear regression models:
1. Linear regression aims to fit a linear function to data to minimize error.
2. Maximum likelihood estimation is equivalent to least squares regression.
3. MAP estimation with a Gaussian prior is equivalent to ridge regression.
4. Linear classification models predict class probabilities using multiple linear functions.
5. The least squares method for classification has disadvantages like being sensitive to outliers.
t-SNE is a modern visualization algorithm that presents high-dimensional data in 2 or 3 dimensions according to some desired distances. If you have some data and you can measure their pairwise differences, t-SNE visualization can help you identify various clusters.
This document discusses gradient descent optimization methods. It begins by explaining where gradient methods are used, such as in regression and machine learning problems. It then introduces several gradient descent algorithms - steepest descent, momentum, Nesterov's accelerated gradient, and others. It provides explanations of how each algorithm works. The document ends by performing benchmarks comparing the algorithms on MNIST data and a regression problem, finding that quasi-Newton and Adam methods tend to work best. In summary, it outlines common gradient descent optimization algorithms and compares their performance on sample problems.
High Dimensional Data Visualization using t-SNEKai-Wen Zhao
Review of the t-SNE algorithm which helps visualizing the high dimensional data on manifold by projecting them onto 2D or 3D space with metric preserving.
This document is a service manual that provides information on the components and operation of an AK45 television. It includes sections describing the tuner, sound and video processing chips, display driver, power supply, microcontroller, and other integrated circuits in the system. The last section discusses service mode adjustments that can be made for production and servicing of the AK45 chassis.
This document provides an overview of preferred natural language processing infrastructure and techniques. It discusses recurrent neural networks, statistical machine translation tools like GIZA++ and Moses, voice recognition systems from NICT and NTT, topic modeling using latent Dirichlet allocation, dependency parsing with minimum spanning trees, and recursive neural networks for natural language tasks. References are provided for several papers on these methods.
My talk at the Stockholm Natural Language Processing Meetup. I explained how word2vec is implemented and how to use it in Python with gensim. When words are represented as points in space, the spatial distance between words describes a similarity between these words. In this talk, I explore how to use this in practice and how to visualize the results (using t-SNE)