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Deep Learning For Data Mining Unsupervised Feature Learning And Representation
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Book Synopsis DEEP LEARNING FOR DATA MINING: UNSUPERVISED FEATURE LEARNING AND REPRESENTATION by : Mr. Srinivas Rao Adabala
Download or read book DEEP LEARNING FOR DATA MINING: UNSUPERVISED FEATURE LEARNING AND REPRESENTATION written by Mr. Srinivas Rao Adabala and published by Xoffencerpublication. This book was released on 2023-08-14 with total page 207 pages. Available in PDF, EPUB and Kindle. Book excerpt: Deep learning has developed as a useful approach for data mining tasks such as unsupervised feature learning and representation. This is thanks to its ability to learn from examples with no prior guidance. Unsupervised learning is the process of discovering patterns and structures in unlabeled data without the use of any explicit labels or annotations. This type of learning does not require the data to be annotated or labelled. This is especially helpful in situations in which labelled data are few or nonexistent. Unsupervised feature learning and representation have seen widespread application of deep learning methods such as auto encoders and generative adversarial networks (GANs). These algorithms learn to describe the data in a hierarchical fashion, where higher-level characteristics are stacked upon lower-level ones, capturing increasingly complicated and abstract patterns as they progress. Neural networks are known as Auto encoders, and they are designed to reconstruct their input data from a compressed representation known as the latent space. The hidden layers of the network are able to learn to encode valuable characteristics that capture the underlying structure of the data when an auto encoder is trained on input that does not have labels attached to it. It is possible to use the reconstruction error as a measurement of how well the auto encoder has learned to represent the data. GANs are made up of two different types of networks: a generator network and a discriminator network. While the discriminator network is taught to differentiate between real and synthetic data, the generator network is taught to generate synthetic data samples that are an accurate representation of the real data. By going through an adversarial training process, both the generator and the discriminator are able to improve their skills. The generator is able to produce more realistic samples, and the discriminator is better able to tell the difference between real and fake samples. One meaningful representation of the data could be understood as being contained within the latent space of the generator. After the deep learning model has learned a reliable representation of the data, it can be put to use for a variety of data mining activities.
Book Synopsis Unsupervised Feature Learning Via Sparse Hierarchical Representations by : Honglak Lee
Download or read book Unsupervised Feature Learning Via Sparse Hierarchical Representations written by Honglak Lee and published by Stanford University. This book was released on 2010 with total page 133 pages. Available in PDF, EPUB and Kindle. Book excerpt: Machine learning has proved a powerful tool for artificial intelligence and data mining problems. However, its success has usually relied on having a good feature representation of the data, and having a poor representation can severely limit the performance of learning algorithms. These feature representations are often hand-designed, require significant amounts of domain knowledge and human labor, and do not generalize well to new domains. To address these issues, I will present machine learning algorithms that can automatically learn good feature representations from unlabeled data in various domains, such as images, audio, text, and robotic sensors. Specifically, I will first describe how efficient sparse coding algorithms --- which represent each input example using a small number of basis vectors --- can be used to learn good low-level representations from unlabeled data. I also show that this gives feature representations that yield improved performance in many machine learning tasks. In addition, building on the deep learning framework, I will present two new algorithms, sparse deep belief networks and convolutional deep belief networks, for building more complex, hierarchical representations, in which more complex features are automatically learned as a composition of simpler ones. When applied to images, this method automatically learns features that correspond to objects and decompositions of objects into object-parts. These features often lead to performance competitive with or better than highly hand-engineered computer vision algorithms in object recognition and segmentation tasks. Further, the same algorithm can be used to learn feature representations from audio data. In particular, the learned features yield improved performance over state-of-the-art methods in several speech recognition tasks.
Author :K. Taylor Publisher :Createspace Independent Publishing Platform ISBN 13 :9781543144567 Total Pages :334 pages Book Rating :4.1/5 (445 download)
Book Synopsis Deep Learning Using MATLAB. Neural Network Applications by : K. Taylor
Download or read book Deep Learning Using MATLAB. Neural Network Applications written by K. Taylor and published by Createspace Independent Publishing Platform. This book was released on 2017-02-16 with total page 334 pages. Available in PDF, EPUB and Kindle. Book excerpt: Deep learning (also known as deep structured learning, hierarchical learning or deep machine learning) is a branch of machine learning based on a set of algorithms that attempt to model high level abstractions in data. Deep learning is part of a broader family of machine learning methods based on learning representations of data. One of the promises of deep learning is replacing handcrafted features with efficient algorithms for unsupervised or semi-supervised feature learning and hierarchical feature extraction. Research in this area attempts to make better representations and create models to learn these representations from large-scale unlabeled data. Some of the representations are inspired by advances in neuroscience and are loosely based on interpretation of information processing and communication patterns in a nervous system, such as neural coding which attempts to define a relationship between various stimuli and associated neuronal responses in the brain. MATLAB has the tool Neural Network Toolbox that provides algorithms, functions, and apps to create, train, visualize, and simulate neural networks. You can perform classification, regression, clustering, dimensionality reduction, time-series forecasting, and dynamic system modeling and control. The toolbox includes convolutional neural network and autoencoder deep learning algorithms for image classification and feature learning tasks. To speed up training of large data sets, you can distribute computations and data across multicore processors, GPUs, and computer clusters using Parallel Computing Toolbox. The more important features are the following: -Deep learning, including convolutional neural networks and autoencoders -Parallel computing and GPU support for accelerating training (with Parallel Computing Toolbox) -Supervised learning algorithms, including multilayer, radial basis, learning vector quantization (LVQ), time-delay, nonlinear autoregressive (NARX), and recurrent neural network (RNN) -Unsupervised learning algorithms, including self-organizing maps and competitive layers -Apps for data-fitting, pattern recognition, and clustering -Preprocessing, postprocessing, and network visualization for improving training efficiency and assessing network performance -Simulink(R) blocks for building and evaluating neural networks and for control systems applications This book develops deep learning, including convolutional neural networks and autoencoders and other types of advanced neural networks
Book Synopsis Feature and Dimensionality Reduction for Clustering with Deep Learning by : Frederic Ros
Download or read book Feature and Dimensionality Reduction for Clustering with Deep Learning written by Frederic Ros and published by Springer Nature. This book was released on 2024-01-22 with total page 273 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents an overview of recent methods of feature selection and dimensionality reduction that are based on Deep Neural Networks (DNNs) for a clustering perspective, with particular attention to the knowledge discovery question. The authors first present a synthesis of the major recent influencing techniques and "tricks" participating in recent advances in deep clustering, as well as a recall of the main deep learning architectures. Secondly, the book highlights the most popular works by “family” to provide a more suitable starting point from which to develop a full understanding of the domain. Overall, the book proposes a comprehensive up-to-date review of deep feature selection and deep clustering methods with particular attention to the knowledge discovery question and under a multi-criteria analysis. The book can be very helpful for young researchers, non-experts, and R&D AI engineers.
Book Synopsis Deep Learning for the Earth Sciences by : Gustau Camps-Valls
Download or read book Deep Learning for the Earth Sciences written by Gustau Camps-Valls and published by John Wiley & Sons. This book was released on 2021-08-18 with total page 436 pages. Available in PDF, EPUB and Kindle. Book excerpt: DEEP LEARNING FOR THE EARTH SCIENCES Explore this insightful treatment of deep learning in the field of earth sciences, from four leading voices Deep learning is a fundamental technique in modern Artificial Intelligence and is being applied to disciplines across the scientific spectrum; earth science is no exception. Yet, the link between deep learning and Earth sciences has only recently entered academic curricula and thus has not yet proliferated. Deep Learning for the Earth Sciences delivers a unique perspective and treatment of the concepts, skills, and practices necessary to quickly become familiar with the application of deep learning techniques to the Earth sciences. The book prepares readers to be ready to use the technologies and principles described in their own research. The distinguished editors have also included resources that explain and provide new ideas and recommendations for new research especially useful to those involved in advanced research education or those seeking PhD thesis orientations. Readers will also benefit from the inclusion of: An introduction to deep learning for classification purposes, including advances in image segmentation and encoding priors, anomaly detection and target detection, and domain adaptation An exploration of learning representations and unsupervised deep learning, including deep learning image fusion, image retrieval, and matching and co-registration Practical discussions of regression, fitting, parameter retrieval, forecasting and interpolation An examination of physics-aware deep learning models, including emulation of complex codes and model parametrizations Perfect for PhD students and researchers in the fields of geosciences, image processing, remote sensing, electrical engineering and computer science, and machine learning, Deep Learning for the Earth Sciences will also earn a place in the libraries of machine learning and pattern recognition researchers, engineers, and scientists.
Book Synopsis Unsupervised Feature Learning Via Sparse Hierarchical Representations by : Honglak Lee
Download or read book Unsupervised Feature Learning Via Sparse Hierarchical Representations written by Honglak Lee and published by . This book was released on 2010 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Machine learning has proved a powerful tool for artificial intelligence and data mining problems. However, its success has usually relied on having a good feature representation of the data, and having a poor representation can severely limit the performance of learning algorithms. These feature representations are often hand-designed, require significant amounts of domain knowledge and human labor, and do not generalize well to new domains. To address these issues, I will present machine learning algorithms that can automatically learn good feature representations from unlabeled data in various domains, such as images, audio, text, and robotic sensors. Specifically, I will first describe how efficient sparse coding algorithms --- which represent each input example using a small number of basis vectors --- can be used to learn good low-level representations from unlabeled data. I also show that this gives feature representations that yield improved performance in many machine learning tasks. In addition, building on the deep learning framework, I will present two new algorithms, sparse deep belief networks and convolutional deep belief networks, for building more complex, hierarchical representations, in which more complex features are automatically learned as a composition of simpler ones. When applied to images, this method automatically learns features that correspond to objects and decompositions of objects into object-parts. These features often lead to performance competitive with or better than highly hand-engineered computer vision algorithms in object recognition and segmentation tasks. Further, the same algorithm can be used to learn feature representations from audio data. In particular, the learned features yield improved performance over state-of-the-art methods in several speech recognition tasks.
Download or read book Machine Learning written by T V Geetha and published by CRC Press. This book was released on 2023-05-17 with total page 593 pages. Available in PDF, EPUB and Kindle. Book excerpt: Machine Learning: Concepts, Techniques and Applications starts at basic conceptual level of explaining machine learning and goes on to explain the basis of machine learning algorithms. The mathematical foundations required are outlined along with their associations to machine learning. The book then goes on to describe important machine learning algorithms along with appropriate use cases. This approach enables the readers to explore the applicability of each algorithm by understanding the differences between them. A comprehensive account of various aspects of ethical machine learning has been discussed. An outline of deep learning models is also included. The use cases, self-assessments, exercises, activities, numerical problems, and projects associated with each chapter aims to concretize the understanding. Features Concepts of Machine learning from basics to algorithms to implementation Comparison of Different Machine Learning Algorithms – When to use them & Why – for Application developers and Researchers Machine Learning from an Application Perspective – General & Machine learning for Healthcare, Education, Business, Engineering Applications Ethics of machine learning including Bias, Fairness, Trust, Responsibility Basics of Deep learning, important deep learning models and applications Plenty of objective questions, Use Cases, Activity and Project based Learning Exercises The book aims to make the thinking of applications and problems in terms of machine learning possible for graduate students, researchers and professionals so that they can formulate the problems, prepare data, decide features, select appropriate machine learning algorithms and do appropriate performance evaluation.
Book Synopsis Experimental Robotics by : Jaydev P. Desai
Download or read book Experimental Robotics written by Jaydev P. Desai and published by Springer. This book was released on 2013-07-09 with total page 966 pages. Available in PDF, EPUB and Kindle. Book excerpt: The International Symposium on Experimental Robotics (ISER) is a series of bi-annual meetings, which are organized, in a rotating fashion around North America, Europe and Asia/Oceania. The goal of ISER is to provide a forum for research in robotics that focuses on novelty of theoretical contributions validated by experimental results. The meetings are conceived to bring together, in a small group setting, researchers from around the world who are in the forefront of experimental robotics research. This unique reference presents the latest advances across the various fields of robotics, with ideas that are not only conceived conceptually but also explored experimentally. It collects robotics contributions on the current developments and new directions in the field of experimental robotics, which are based on the papers presented at the 13the ISER held in Québec City, Canada, at the Fairmont Le Château Frontenac, on June 18-21, 2012. This present thirteenth edition of Experimental Robotics edited by Jaydev P. Desai, Gregory Dudek, Oussama Khatib, and Vijay Kumar offers a collection of a broad range of topics in field and human-centered robotics.
Book Synopsis Demystifying Unsupervised Feature Learning by : Adam Paul Coates
Download or read book Demystifying Unsupervised Feature Learning written by Adam Paul Coates and published by . This book was released on 2012 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Machine learning is a key component of state-of-the-art systems in many application domains. Applied to many kinds of raw data, however, most learning algorithms are unable to make good predictions. In order to succeed, most learning algorithms are applied instead to "features" that represent higher-level concepts extracted from the raw data. These features, developed by expert practitioners in each field, encode important prior knowledge about the task that the learning algorithm would be unable to discover on its own from (often limited) labeled training examples. Unfortunately, engineering good feature representations for new applications is extremely difficult. For the most challenging applications in AI, like computer vision, the search for good features and higher-level image representations is vast and ongoing. In this work we study a class of algorithms that attempt to learn feature representations automatically from unlabeled data that is often easy to obtain in large quantities. Though many such algorithms have been proposed and have achieved high marks on benchmark tasks, it has not been fully understood what causes some algorithms to perform well and others to perform poorly. It has thus been difficult to identify any key directions in which the algorithms might be improved in order to significantly advance the state of the art. To address this issue, we will present results from an in-depth scientific study of a variety of factors that can affect the performance of feature-learning algorithms. Through a detailed analysis, a surprising picture emerges: we find that many schemes succeed or fail as a result of a few (easily overlooked) factors that are often orthogonal to the particular learning methods involved. In fact, by focusing solely on these factors it is possible to achieve state-of-the-art performance on common benchmarks using quite simple algorithms. More importantly, however, a main contribution of this line of research has been to identify very simple yet highly scalable feature learning methods that, by virtue of focusing on the most critical properties identified in our study, are highly successful in many settings: the proposed algorithms consistently achieve top performance on benchmarks, have been successfully deployed in realistic computer vision applications, and are even capable of discovering high-level concepts like object classes without any supervision.
Book Synopsis Unsupervised Learning Approaches for Dimensionality Reduction and Data Visualization by : B.K. Tripathy
Download or read book Unsupervised Learning Approaches for Dimensionality Reduction and Data Visualization written by B.K. Tripathy and published by CRC Press. This book was released on 2021-09-01 with total page 174 pages. Available in PDF, EPUB and Kindle. Book excerpt: Unsupervised Learning Approaches for Dimensionality Reduction and Data Visualization describes such algorithms as Locally Linear Embedding (LLE), Laplacian Eigenmaps, Isomap, Semidefinite Embedding, and t-SNE to resolve the problem of dimensionality reduction in the case of non-linear relationships within the data. Underlying mathematical concepts, derivations, and proofs with logical explanations for these algorithms are discussed, including strengths and limitations. The book highlights important use cases of these algorithms and provides examples along with visualizations. Comparative study of the algorithms is presented to give a clear idea on selecting the best suitable algorithm for a given dataset for efficient dimensionality reduction and data visualization. FEATURES Demonstrates how unsupervised learning approaches can be used for dimensionality reduction Neatly explains algorithms with a focus on the fundamentals and underlying mathematical concepts Describes the comparative study of the algorithms and discusses when and where each algorithm is best suitable for use Provides use cases, illustrative examples, and visualizations of each algorithm Helps visualize and create compact representations of high dimensional and intricate data for various real-world applications and data analysis This book is aimed at professionals, graduate students, and researchers in Computer Science and Engineering, Data Science, Machine Learning, Computer Vision, Data Mining, Deep Learning, Sensor Data Filtering, Feature Extraction for Control Systems, and Medical Instruments Input Extraction.
Book Synopsis Prediction and Analysis for Knowledge Representation and Machine Learning by : Avadhesh Kumar
Download or read book Prediction and Analysis for Knowledge Representation and Machine Learning written by Avadhesh Kumar and published by CRC Press. This book was released on 2022-01-31 with total page 216 pages. Available in PDF, EPUB and Kindle. Book excerpt: A number of approaches are being defined for statistics and machine learning. These approaches are used for the identification of the process of the system and the models created from the system’s perceived data, assisting scientists in the generation or refinement of current models. Machine learning is being studied extensively in science, particularly in bioinformatics, economics, social sciences, ecology, and climate science, but learning from data individually needs to be researched more for complex scenarios. Advanced knowledge representation approaches that can capture structural and process properties are necessary to provide meaningful knowledge to machine learning algorithms. It has a significant impact on comprehending difficult scientific problems. Prediction and Analysis for Knowledge Representation and Machine Learning demonstrates various knowledge representation and machine learning methodologies and architectures that will be active in the research field. The approaches are reviewed with real-life examples from a wide range of research topics. An understanding of a number of techniques and algorithms that are implemented in knowledge representation in machine learning is available through the book’s website. Features: Examines the representational adequacy of needed knowledge representation Manipulates inferential adequacy for knowledge representation in order to produce new knowledge derived from the original information Improves inferential and acquisition efficiency by applying automatic methods to acquire new knowledge Covers the major challenges, concerns, and breakthroughs in knowledge representation and machine learning using the most up-to-date technology Describes the ideas of knowledge representation and related technologies, as well as their applications, in order to help humankind become better and smarter This book serves as a reference book for researchers and practitioners who are working in the field of information technology and computer science in knowledge representation and machine learning for both basic and advanced concepts. Nowadays, it has become essential to develop adaptive, robust, scalable, and reliable applications and also design solutions for day-to-day problems. The edited book will be helpful for industry people and will also help beginners as well as high-level users for learning the latest things, which includes both basic and advanced concepts.
Book Synopsis Machine Learning by : Kamal Kant Hiran
Download or read book Machine Learning written by Kamal Kant Hiran and published by BPB Publications. This book was released on 2021-09-16 with total page 309 pages. Available in PDF, EPUB and Kindle. Book excerpt: Concepts of Machine Learning with Practical Approaches. KEY FEATURES ● Includes real-scenario examples to explain the working of Machine Learning algorithms. ● Includes graphical and statistical representation to simplify modeling Machine Learning and Neural Networks. ● Full of Python codes, numerous exercises, and model question papers for data science students. DESCRIPTION The book offers the readers the fundamental concepts of Machine Learning techniques in a user-friendly language. The book aims to give in-depth knowledge of the different Machine Learning (ML) algorithms and the practical implementation of the various ML approaches. This book covers different Supervised Machine Learning algorithms such as Linear Regression Model, Naïve Bayes classifier Decision Tree, K-nearest neighbor, Logistic Regression, Support Vector Machine, Random forest algorithms, Unsupervised Machine Learning algorithms such as k-means clustering, Hierarchical Clustering, Probabilistic clustering, Association rule mining, Apriori Algorithm, f-p growth algorithm, Gaussian mixture model and Reinforcement Learning algorithm such as Markov Decision Process (MDP), Bellman equations, policy evaluation using Monte Carlo, Policy iteration and Value iteration, Q-Learning, State-Action-Reward-State-Action (SARSA). It also includes various feature extraction and feature selection techniques, the Recommender System, and a brief overview of Deep Learning. By the end of this book, the reader can understand Machine Learning concepts and easily implement various ML algorithms to real-world problems. WHAT YOU WILL LEARN ● Perform feature extraction and feature selection techniques. ● Learn to select the best Machine Learning algorithm for a given problem. ● Get a stronghold in using popular Python libraries like Scikit-learn, pandas, and matplotlib. ● Practice how to implement different types of Machine Learning techniques. ● Learn about Artificial Neural Network along with the Back Propagation Algorithm. ● Make use of various recommended systems with powerful algorithms. WHO THIS BOOK IS FOR This book is designed for data science and analytics students, academicians, and researchers who want to explore the concepts of machine learning and practice the understanding of real cases. Knowing basic statistical and programming concepts would be good, although not mandatory. TABLE OF CONTENTS 1. Introduction 2. Supervised Learning Algorithms 3. Unsupervised Learning 4. Introduction to the Statistical Learning Theory 5. Semi-Supervised Learning and Reinforcement Learning 6. Recommended Systems
Book Synopsis Machine Learning Foundations by : Taeho Jo
Download or read book Machine Learning Foundations written by Taeho Jo and published by Springer Nature. This book was released on 2021-02-12 with total page 391 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides conceptual understanding of machine learning algorithms though supervised, unsupervised, and advanced learning techniques. The book consists of four parts: foundation, supervised learning, unsupervised learning, and advanced learning. The first part provides the fundamental materials, background, and simple machine learning algorithms, as the preparation for studying machine learning algorithms. The second and the third parts provide understanding of the supervised learning algorithms and the unsupervised learning algorithms as the core parts. The last part provides advanced machine learning algorithms: ensemble learning, semi-supervised learning, temporal learning, and reinforced learning. Provides comprehensive coverage of both learning algorithms: supervised and unsupervised learning; Outlines the computation paradigm for solving classification, regression, and clustering; Features essential techniques for building the a new generation of machine learning.
Book Synopsis Deep Learning for Data Analytics by : Himansu Das
Download or read book Deep Learning for Data Analytics written by Himansu Das and published by Academic Press. This book was released on 2020-05-29 with total page 220 pages. Available in PDF, EPUB and Kindle. Book excerpt: Deep learning, a branch of Artificial Intelligence and machine learning, has led to new approaches to solving problems in a variety of domains including data science, data analytics and biomedical engineering. Deep Learning for Data Analytics: Foundations, Biomedical Applications and Challenges provides readers with a focused approach for the design and implementation of deep learning concepts using data analytics techniques in large scale environments. Deep learning algorithms are based on artificial neural network models to cascade multiple layers of nonlinear processing, which aids in feature extraction and learning in supervised and unsupervised ways, including classification and pattern analysis. Deep learning transforms data through a cascade of layers, helping systems analyze and process complex data sets. Deep learning algorithms extract high level complex data and process these complex sets to relatively simpler ideas formulated in the preceding level of the hierarchy. The authors of this book focus on suitable data analytics methods to solve complex real world problems such as medical image recognition, biomedical engineering, and object tracking using deep learning methodologies. The book provides a pragmatic direction for researchers who wish to analyze large volumes of data for business, engineering, and biomedical applications. Deep learning architectures including deep neural networks, recurrent neural networks, and deep belief networks can be used to help resolve problems in applications such as natural language processing, speech recognition, computer vision, bioinoformatics, audio recognition, drug design, and medical image analysis. Presents the latest advances in Deep Learning for data analytics and biomedical engineering applications. Discusses Deep Learning techniques as they are being applied in the real world of biomedical engineering and data science, including Deep Learning networks, deep feature learning, deep learning toolboxes, performance evaluation, Deep Learning optimization, deep auto-encoders, and deep neural networks Provides readers with an introduction to Deep Learning, along with coverage of deep belief networks, convolutional neural networks, Restricted Boltzmann Machines, data analytics basics, enterprise data science, predictive analysis, optimization for Deep Learning, and feature selection using Deep Learning
Book Synopsis Deep Learning by : Siddhartha Bhattacharyya
Download or read book Deep Learning written by Siddhartha Bhattacharyya and published by Walter de Gruyter GmbH & Co KG. This book was released on 2020-06-22 with total page 208 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book focuses on the fundamentals of deep learning along with reporting on the current state-of-art research on deep learning. In addition, it provides an insight of deep neural networks in action with illustrative coding examples. Deep learning is a new area of machine learning research which has been introduced with the objective of moving ML closer to one of its original goals, i.e. artificial intelligence. Deep learning was developed as an ML approach to deal with complex input-output mappings. While traditional methods successfully solve problems where final value is a simple function of input data, deep learning techniques are able to capture composite relations between non-immediately related fields, for example between air pressure recordings and English words, millions of pixels and textual description, brand-related news and future stock prices and almost all real world problems. Deep learning is a class of nature inspired machine learning algorithms that uses a cascade of multiple layers of nonlinear processing units for feature extraction and transformation. Each successive layer uses the output from the previous layer as input. The learning may be supervised (e.g. classification) and/or unsupervised (e.g. pattern analysis) manners. These algorithms learn multiple levels of representations that correspond to different levels of abstraction by resorting to some form of gradient descent for training via backpropagation. Layers that have been used in deep learning include hidden layers of an artificial neural network and sets of propositional formulas. They may also include latent variables organized layer-wise in deep generative models such as the nodes in deep belief networks and deep boltzmann machines. Deep learning is part of state-of-the-art systems in various disciplines, particularly computer vision, automatic speech recognition (ASR) and human action recognition.
Book Synopsis Scalable Feature Learning by : Quoc V. Le
Download or read book Scalable Feature Learning written by Quoc V. Le and published by . This book was released on 2013 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Over the past decade, machine learning has emerged as a powerful methodology that empowers autonomous decision making by learning and generalizing from examples. Thanks to machine learning, we now have software that classifies spam emails, recog- nizes faces from images, recommends movies and books. Despite this success, machine learning often requires a large amount of labeled data and significant manual feature engineering. For example, it is difficult to design algorithms that can recognize ob- jects from images as well as humans can. This difficulty is due to the fact that data are high-dimensional (a small 100x100 pixel image is often represented as 10,000 di- mensional vector) and highly-variable (due to many factors of transformations such as translation, rotation, illumination, scaling, viewpoint changes). To simplify this task, it is often necessary to construct features which are invariant to transformations. Features have become the lens that machine learning algorithms see the world. Despite its importance to machine learning and A.I., the process of construct- ing features is typically carried out by human experts and requires a great deal of knowledge and time, typically years. Even worse, these features may only work on a restricted set of problems and it can be difficult to generalize them for other do- mains. It is generally believed that automating the process of creating features is an important step to move A.I. and machine learning forward. Deep learning and unsupervised feature learning have shown great promises as methods to overcome manual feature engineering by learning features from data. However, these methods have been fundamentally limited by our computational abil- ities, and typically applied to small-sized problems. My recent work on deep learning and unsupervised feature learning has mainly focused on addressing their scalability, especially when applied to big data. In particular, my work tackles fundamental challenges when scaling up these algorithms by i) simplifying their optimization problems, ii) enabling model parallelism via sparse network connections, iii) enabling robust data parallelism by relaxing synchronization in optimization. The details of these techniques are described below Making deep learning simple: While certain classes of unsupervised feature learning algorithms, such as Independent Component Analysis (ICA), are effective in learning feature representations from data, they are difficult to optimize. To ad- dress this, we developed a simplified training method, known as RICA, by introducing a reconstruction penalty as a replacement for orthogonalization. Via a sequence of mathematical equivalences, we proved that RICA is equivalent to the original ICA op- timization problem under certain hyperparameter settings. The new approach how- ever has more freedom to learn overcomplete representations and converges faster. Our proof also shows connections between ICA and other deep learning approaches (sparse coding, RBMs, autoencoders etc.). Our algorithm, RICA, in addition to being scalable and able to learn invariant features, can be used to learn features for different domains. We have succeeded in applying the algorithms to learn features to identify activities in videos and cancers in MRI medical images. The learned representations are now state-of-the-art in both domains. Enabling model parallelism via model partitioning: A major weakness of deep learning algorithms, including RICA, is that they can be slow when applied to large problems. This is due to the fact that in standard deep learning models, every feature connects to every input dimension, e.g., every feature on a 100x100 pixel image is a 10,000 dimensional vector. This fundamental weakness hinders our understanding of the deep learning's potential when applied to real problems. To address this weakness, I have also worked on methods to scale up deep learning al- gorithms to big data. To this end, I proposed the idea of tiling local receptive fields to significantly reduce the number of parameters in a deep model. Specifically, each feature in our models only connects to a small area of the input data (local receptive fields). To further reduce the parameters, features that are far away can share the weights. Unlike convolutional models, adjacent receptive fields may not share pa- rameters. This flexibility allows the model to learn more invariant properties of the data more than just translational invariances typically achieved by full weight sharing in convolutional models. Visualization shows that tiling RICA can learn rotational, scaling and translational invariances from unlabeled data. In addition to reducing the number of parameters, local receptive fields also allow model partitioning and thus parallelism. This can be achieved by splitting the feature computations for non-overlapped areas of input data to different machines. This scheme of model partitioning (also known as model parallelism) enables the use of hundreds of machines to compute and train features. While this approach works well with hundreds of machines, scaling further can be difficult. This is because the entire system may have to wait for one slow machine and the chance of having one slow machine goes up as we use more machines. In practice, we use model partitioning in combination with asynchronous stochastic gradient descent described below. Enabling data parallelism via asynchronous SGD: I have also contributed to the development of asynchronous stochastic gradient descent (SGD) for scaling up deep learning models using thousands of machines. In detail, the previous approach of parallelizing deep learning is to train multiple model replicas (each with model partitioning as described above) and then communicate parameters via a central server called master. The communication is typically synchronous: the master has to wait for messages from all slaves before computing updates; all slaves have to wait for the message from the master to perform new computations. This mechanism has a weakness that if one of the slave is slow, the entire training procedure is slow. We found that asynchronous communications address this problem. In particular, the master updates its parameters as long as it receives a message from the slave and vice versa. Even though messages can be out-of-date (e.g., gradient being computed on delayed parameters), the method works well, lets us scale to thousands of machines and is much faster than conventional synchronous updates.
Book Synopsis Learning Deep Architectures for AI by : Yoshua Bengio
Download or read book Learning Deep Architectures for AI written by Yoshua Bengio and published by Now Publishers Inc. This book was released on 2009 with total page 145 pages. Available in PDF, EPUB and Kindle. Book excerpt: Theoretical results suggest that in order to learn the kind of complicated functions that can represent high-level abstractions (e.g. in vision, language, and other AI-level tasks), one may need deep architectures. Deep architectures are composed of multiple levels of non-linear operations, such as in neural nets with many hidden layers or in complicated propositional formulae re-using many sub-formulae. Searching the parameter space of deep architectures is a difficult task, but learning algorithms such as those for Deep Belief Networks have recently been proposed to tackle this problem with notable success, beating the state-of-the-art in certain areas. This paper discusses the motivations and principles regarding learning algorithms for deep architectures, in particular those exploiting as building blocks unsupervised learning of single-layer models such as Restricted Boltzmann Machines, used to construct deeper models such as Deep Belief Networks.
