On Optimization And Scalability In Deep Learning
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On Optimization And Scalability In Deep Learning
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Author : Kenji Kawaguchi (Ph. D.)
language : en
Publisher:
Release Date : 2020
On Optimization And Scalability In Deep Learning written by Kenji Kawaguchi (Ph. D.) and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020 with categories.
Deep neural networks have achieved significant empirical success in many fields, including computer vision, machine learning, and artificial intelligence. Along with its empirical success, deep learning has been theoretically shown to be attractive in terms of its expressive power. That is, neural networks with one hidden layer can approximate any continuous function, and deeper neural networks can approximate functions of certain classes with fewer parameters. Expressivity theory states that there exist optimal parameter vectors for neural networks of certain sizes to approximate desired target functions. However, the expressivity theory does not ensure that we can find such an optimal vector efficiently during optimization of a neural network. Optimization is one of the key steps in deep learning because learning from data is achieved through optimization, i.e., the process of optimizing the parameters of a deep neural network to make the network consistent with the data. This process typically requires nonconvex optimization, which is not scalable for high-dimensional problems in general. Indeed, in general, optimization of a neural network is not scalable without additional assumptions on its architecture. This thesis studies the non-convex optimization of various architectures of deep neural networks by focusing on some fundamental bottlenecks in the scalability, such as suboptimal local minima and saddle points. In particular, for deep neural networks, we present various guarantees for the values of local minima and critical points, as well as for points found by gradient descent. We prove that mild over-parameterization of practical degrees can ensure that gradient descent will find a global minimum for non-convex optimization of deep neural networks. Furthermore, even without over-parameterization, we show, both theoretically and empirically, that increasing the number of parameters improves the values of critical points and local minima towards the global minimum value. We also prove theoretical guarantees on the values of local minima for residual neural networks. Moreover, this thesis presents a unified theory to analyze the critical points and local minima of various deep neural networks beyond these specific architectures. These results suggest that, whereas there is the issue of scalability in the theoretical worst-case and worst architectures, we can avoid the issue and scale well for large problems with various useful architectures in practice.
Scalable And Distributed Machine Learning And Deep Learning Patterns
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Author : Thomas, J. Joshua
language : en
Publisher: IGI Global
Release Date : 2023-08-25
Scalable And Distributed Machine Learning And Deep Learning Patterns written by Thomas, J. Joshua and has been published by IGI Global this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-08-25 with Computers categories.
Scalable and Distributed Machine Learning and Deep Learning Patterns is a practical guide that provides insights into how distributed machine learning can speed up the training and serving of machine learning models, reduce time and costs, and address bottlenecks in the system during concurrent model training and inference. The book covers various topics related to distributed machine learning such as data parallelism, model parallelism, and hybrid parallelism. Readers will learn about cutting-edge parallel techniques for serving and training models such as parameter server and all-reduce, pipeline input, intra-layer model parallelism, and a hybrid of data and model parallelism. The book is suitable for machine learning professionals, researchers, and students who want to learn about distributed machine learning techniques and apply them to their work. This book is an essential resource for advancing knowledge and skills in artificial intelligence, deep learning, and high-performance computing. The book is suitable for computer, electronics, and electrical engineering courses focusing on artificial intelligence, parallel computing, high-performance computing, machine learning, and its applications. Whether you're a professional, researcher, or student working on machine and deep learning applications, this book provides a comprehensive guide for creating distributed machine learning, including multi-node machine learning systems, using Python development experience. By the end of the book, readers will have the knowledge and abilities necessary to construct and implement a distributed data processing pipeline for machine learning model inference and training, all while saving time and costs.
Stochastic Optimization For Large Scale Machine Learning
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Author : Vinod Kumar Chauhan
language : en
Publisher: CRC Press
Release Date : 2021-11-18
Stochastic Optimization For Large Scale Machine Learning written by Vinod Kumar Chauhan and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-11-18 with Computers categories.
Advancements in the technology and availability of data sources have led to the `Big Data' era. Working with large data offers the potential to uncover more fine-grained patterns and take timely and accurate decisions, but it also creates a lot of challenges such as slow training and scalability of machine learning models. One of the major challenges in machine learning is to develop efficient and scalable learning algorithms, i.e., optimization techniques to solve large scale learning problems. Stochastic Optimization for Large-scale Machine Learning identifies different areas of improvement and recent research directions to tackle the challenge. Developed optimisation techniques are also explored to improve machine learning algorithms based on data access and on first and second order optimisation methods. Key Features: Bridges machine learning and Optimisation. Bridges theory and practice in machine learning. Identifies key research areas and recent research directions to solve large-scale machine learning problems. Develops optimisation techniques to improve machine learning algorithms for big data problems. The book will be a valuable reference to practitioners and researchers as well as students in the field of machine learning.
Scalable Feature Learning
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Author : Quoc V. Le
language : en
Publisher:
Release Date : 2013
Scalable Feature Learning written by Quoc V. Le and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013 with categories.
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.
Scaling Machine Learning With Spark
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Author : Adi Polak
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2023-03-07
Scaling Machine Learning With Spark written by Adi Polak and has been published by "O'Reilly Media, Inc." this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-03-07 with Computers categories.
Learn how to build end-to-end scalable machine learning solutions with Apache Spark. With this practical guide, author Adi Polak introduces data and ML practitioners to creative solutions that supersede today's traditional methods. You'll learn a more holistic approach that takes you beyond specific requirements and organizational goals--allowing data and ML practitioners to collaborate and understand each other better. Scaling Machine Learning with Spark examines several technologies for building end-to-end distributed ML workflows based on the Apache Spark ecosystem with Spark MLlib, MLflow, TensorFlow, and PyTorch. If you're a data scientist who works with machine learning, this book shows you when and why to use each technology. You will: Explore machine learning, including distributed computing concepts and terminology Manage the ML lifecycle with MLflow Ingest data and perform basic preprocessing with Spark Explore feature engineering, and use Spark to extract features Train a model with MLlib and build a pipeline to reproduce it Build a data system to combine the power of Spark with deep learning Get a step-by-step example of working with distributed TensorFlow Use PyTorch to scale machine learning and its internal architecture
Deep Learning At Scale
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Author : Suneeta Mall
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2024-06-18
Deep Learning At Scale written by Suneeta Mall and has been published by "O'Reilly Media, Inc." this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-06-18 with Computers categories.
Bringing a deep-learning project into production at scale is quite challenging. To successfully scale your project, a foundational understanding of full stack deep learning, including the knowledge that lies at the intersection of hardware, software, data, and algorithms, is required. This book illustrates complex concepts of full stack deep learning and reinforces them through hands-on exercises to arm you with tools and techniques to scale your project. A scaling effort is only beneficial when it's effective and efficient. To that end, this guide explains the intricate concepts and techniques that will help you scale effectively and efficiently. You'll gain a thorough understanding of: How data flows through the deep-learning network and the role the computation graphs play in building your model How accelerated computing speeds up your training and how best you can utilize the resources at your disposal How to train your model using distributed training paradigms, i.e., data, model, and pipeline parallelism How to leverage PyTorch ecosystems in conjunction with NVIDIA libraries and Triton to scale your model training Debugging, monitoring, and investigating the undesirable bottlenecks that slow down your model training How to expedite the training lifecycle and streamline your feedback loop to iterate model development A set of data tricks and techniques and how to apply them to scale your training model How to select the right tools and techniques for your deep-learning project Options for managing the compute infrastructure when running at scale
Scalable Optimization Via Probabilistic Modeling
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Author : Martin Pelikan
language : en
Publisher: Springer
Release Date : 2007-01-12
Scalable Optimization Via Probabilistic Modeling written by Martin Pelikan and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2007-01-12 with Mathematics categories.
I’m not usually a fan of edited volumes. Too often they are an incoherent hodgepodge of remnants, renegades, or rejects foisted upon an unsuspecting reading public under a misleading or fraudulent title. The volume Scalable Optimization via Probabilistic Modeling: From Algorithms to Applications is a worthy addition to your library because it succeeds on exactly those dimensions where so many edited volumes fail. For example, take the title, Scalable Optimization via Probabilistic M- eling: From Algorithms to Applications. You need not worry that you’re going to pick up this book and ?nd stray articles about anything else. This book focuseslikealaserbeamononeofthehottesttopicsinevolutionary compu- tion over the last decade or so: estimation of distribution algorithms (EDAs). EDAs borrow evolutionary computation’s population orientation and sel- tionism and throw out the genetics to give us a hybrid of substantial power, elegance, and extensibility. The article sequencing in most edited volumes is hard to understand, but from the get go the editors of this volume have assembled a set of articles sequenced in a logical fashion. The book moves from design to e?ciency enhancement and then concludes with relevant applications. The emphasis on e?ciency enhancement is particularly important, because the data-mining perspectiveimplicitinEDAsopensuptheworldofoptimizationtonewme- ods of data-guided adaptation that can further speed solutions through the construction and utilization of e?ective surrogates, hybrids, and parallel and temporal decompositions.
Scaling Up Machine Learning
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Author : Ron Bekkerman
language : en
Publisher: Cambridge University Press
Release Date : 2012
Scaling Up Machine Learning written by Ron Bekkerman and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012 with Computers categories.
This integrated collection covers a range of parallelization platforms, concurrent programming frameworks and machine learning settings, with case studies.
Advances In Scaling Deep Learning Algorithms
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Author : Yann Dauphin
language : en
Publisher:
Release Date : 2015
Advances In Scaling Deep Learning Algorithms written by Yann Dauphin and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015 with categories.
Designing Deep Learning Systems
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Author : Chi Wang
language : en
Publisher: Simon and Schuster
Release Date : 2023-09-19
Designing Deep Learning Systems written by Chi Wang and has been published by Simon and Schuster this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-09-19 with Computers categories.
A vital guide to building the platforms and systems that bring deep learning models to production. In Designing Deep Learning Systems you will learn how to: Transfer your software development skills to deep learning systems Recognize and solve common engineering challenges for deep learning systems Understand the deep learning development cycle Automate training for models in TensorFlow and PyTorch Optimize dataset management, training, model serving and hyperparameter tuning Pick the right open-source project for your platform Deep learning systems are the components and infrastructure essential to supporting a deep learning model in a production environment. Written especially for software engineers with minimal knowledge of deep learning’s design requirements, Designing Deep Learning Systems is full of hands-on examples that will help you transfer your software development skills to creating these deep learning platforms. You’ll learn how to build automated and scalable services for core tasks like dataset management, model training/serving, and hyperparameter tuning. This book is the perfect way to step into an exciting—and lucrative—career as a deep learning engineer. About the technology To be practically usable, a deep learning model must be built into a software platform. As a software engineer, you need a deep understanding of deep learning to create such a system. Th is book gives you that depth. About the book Designing Deep Learning Systems: A software engineer's guide teaches you everything you need to design and implement a production-ready deep learning platform. First, it presents the big picture of a deep learning system from the developer’s perspective, including its major components and how they are connected. Then, it carefully guides you through the engineering methods you’ll need to build your own maintainable, efficient, and scalable deep learning platforms. What's inside The deep learning development cycle Automate training in TensorFlow and PyTorch Dataset management, model serving, and hyperparameter tuning A hands-on deep learning lab About the reader For software developers and engineering-minded data scientists. Examples in Java and Python. About the author Chi Wang is a principal software developer in the Salesforce Einstein group. Donald Szeto was the co-founder and CTO of PredictionIO. Table of Contents 1 An introduction to deep learning systems 2 Dataset management service 3 Model training service 4 Distributed training 5 Hyperparameter optimization service 6 Model serving design 7 Model serving in practice 8 Metadata and artifact store 9 Workflow orchestration 10 Path to production