Deep learning is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts. Because the computer gathers knowledge from experience, there is no need for a human computer operator to formally specify all the knowledge that the computer needs. The hierarchy of concepts allows the computer to learn complicated concepts by building them out of simpler ones; a graph of these hierarchies would be many layers deep. This book introduces a broad range of topics in deep learning. The text offers mathematical and conceptual background, covering relevant concepts in linear algebra, probability theory and information theory, numerical computation, and machine learning. It describes deep learning techniques used by practitioners in industry, including deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical methodology; and it surveys such applications as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames. Finally, the book offers research perspectives, covering such theoretical topics as linear factor models, autoencoders, representation learning, structured probabilistic models, Monte Carlo methods, the partition function, approximate inference, and deep generative models. Deep Learning can be used by undergraduate or graduate students planning careers in either industry or research, and by software engineers who want to begin using deep learning in their products or platforms. A website offers supplementary material for both readers and instructors.

Deep Learning

We describe latent Dirichlet allocation (LDA), a generative probabilistic model for collections of discrete data such as text corpora. LDA is a three-level hierarchical Bayesian model, in which each item of a collection is modeled as a finite mixture over an underlying set of topics. Each topic is, in turn, modeled as an infinite mixture over an underlying set of topic probabilities. In the context of text modeling, the topic probabilities provide an explicit representation of a document. We present efficient approximate inference techniques based on variational methods and an EM algorithm for empirical Bayes parameter estimation. We report results in document modeling, text classification, and collaborative filtering, comparing to a mixture of unigrams model and the probabilistic LSI model.

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Latent dirichlet allocation

We propose a generative model for text and other collections of discrete data that generalizes or improves on several previous models including naive Bayes/unigram, mixture of unigrams [6], and Hof-mann's aspect model, also known as probabilistic latent semantic indexing (pLSI) [3]. In the context of text modeling, our model posits that each document is generated as a mixture of topics, where the continuous-valued mixture proportions are distributed as a latent Dirichlet random variable. Inference and learning are carried out efficiently via variational algorithms. We present empirical results on applications of this model to problems in text modeling, collaborative filtering, and text classification.

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Latent Dirichlet Allocation

Data Mining: Practical Machine Learning Tools and Techniques offers a thorough grounding in machine learning concepts as well as practical advice on applying machine learning tools and techniques in real-world data mining situations. This highly anticipated third edition of the most acclaimed work on data mining and machine learning will teach you everything you need to know about preparing inputs, interpreting outputs, evaluating results, and the algorithmic methods at the heart of successful data mining. Thorough updates reflect the technical changes and modernizations that have taken place in the field since the last edition, including new material on Data Transformations, Ensemble Learning, Massive Data Sets, Multi-instance Learning, plus a new version of the popular Weka machine learning software developed by the authors. Witten, Frank, and Hall include both tried-and-true techniques of today as well as methods at the leading edge of contemporary research. *Provides a thorough grounding in machine learning concepts as well as practical advice on applying the tools and techniques to your data mining projects *Offers concrete tips and techniques for performance improvement that work by transforming the input or output in machine learning methods *Includes downloadable Weka software toolkit, a collection of machine learning algorithms for data mining tasks-in an updated, interactive interface. Algorithms in toolkit cover: data pre-processing, classification, regression, clustering, association rules, visualization

Data Mining: Practical Machine Learning Tools and Techniques

抗原变异可使得多种致病微生物易于逃避宿主免疫应答。表达在感染红细胞表面的恶性疟原虫红细胞表面蛋白1（PfPMP1）与感染红细胞、内皮细胞、树突状细胞以及胎盘的单个或多个受体作用，在黏附及免疫逃避中起关键的作用。每个单倍体基因组var基因家族编码约60种成员，通过启动转录不同的var基因变异体为抗原变异提供了分子基础。

疟原虫var基因转换速率变化导致抗原变异[英]／Paul H, Robert P, Christodoulou Z, et al//Proc Natl Acad Sci U S A

The ability to simultaneously localise a robot and accurately map its surroundings is considered by many to be a key prerequisite of truly autonomous robots. This paper presents a real-world implementation of FastSLAM, an algorithm that recursively estimates the full posterior distribution of both robot pose and landmark locations. In particular, we present an extension to FastSLAM that addresses the data association problem using a nearest neighbor technique. Building on this, we also present a novel multiple hypotheses tracking implementation (MHT) to handle uncertainty in the data association. Finally an extension to the multi-robot case is introduced. Our algorithm has been run successfully using a number of data sets obtained in outdoor environments. Experimental results are presented that demonstrate the performance of the algorithms when compared with standard Kalman filter-based approaches.

Real time data association for FastSLAM

Kalman filters are a workhorse of robotics and are routinely used in state-estimation problems. However, their performance critically depends on a large number of modeling parameters which can be very difficult to obtain, and are often set via significant manual tweaking and at a great cost of engineering time. In this paper, we propose a method for automatically learning the noise parameters of a Kalman filter. We also demonstrate on a commercial wheeled rover that our Kalman filter’s learned noise covariance parameters—obtained quickly and fully automatically—significantly outperform an earlier, carefully and laboriously hand-designed one.

http://www.roboticsproceedings.org/rss01/p38.pdf

Discriminative Training of Kalman Filters.

Motion and path-planning algorithms often use complex cost functions for both global navigation and local smoothing of trajectories. Obtaining good results typically requires carefully hand-engineering the trade-offs between different terms in the cost function. In practice, it is often much easier to demonstrate a few good trajectories. In this paper, we describe an efficient algorithm which - when given access to a few trajectory demonstrations - can automatically infer good trade-offs between the different costs. In our experiments, we apply our algorithm to the problem of navigating a robotic car in a parking lot.

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Apprenticeship learning for motion planning with application to parking lot navigation

In [Thrun, S., et al., 2001], we proposed the sparse extended information filter for efficiently solving the simultaneous localization and mapping (SLAM) problem. In this paper, we extend this algorithm to handle data association problems and report real-world results, obtained with an outdoor vehicle. We find that our approach performs favorably when compared to the extended Kalman filter solution from which it is derived.

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Results for outdoor-SLAM using sparse extended information filters

Presents a way to acquire occupancy grid maps with mobile robots. Virtually all existing occupancy grid mapping algorithms decompose the high-dimensional mapping problem into a collection of one-dimensional problems, where the occupancy of each grid cell is estimated independently of others. This induces conflicts that can lead to inconsistent maps. The paper shows how to solve the mapping problem in the original, high-dimensional space, thereby maintaining all dependencies between neighboring cells. As a result, maps generated by our approach are often more accurate than those generated using traditional techniques. Our approach relies on a rigorous statistical formulation of the mapping problem using forward models. It employs the expectation maximization algorithm for estimating maps, and a Laplacian approximation to determine uncertainty.

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Sebastian Thrun

Papers

Real time data association for FastSLAM

Discriminative Training of Kalman Filters.

Apprenticeship learning for motion planning with application to parking lot navigation

Results for outdoor-SLAM using sparse extended information filters

Learning occupancy grids with forward models