Rethinking Methods to Train Deep Neural Networks

2019

Author : Wendy Wei (M. Eng.)
Release : 2019
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Book Synopsis Rethinking Methods to Train Deep Neural Networks by : Wendy Wei (M. Eng.)

Download or read book Rethinking Methods to Train Deep Neural Networks written by Wendy Wei (M. Eng.). This book was released on 2019. Available in PDF, EPUB and Kindle. Book excerpt: Deep neural networks are known to be highly non-convex. Many of the methods used in deep learning which are informed by convex optimization work surprisingly well. The training dynamics of optimization methods such as momentum suggest that training occurs in distinct regimes, attributed to learning rate. In the low learning rate regime, many convex intuitions hold, and the recommended methods are able to reach a good solution. In the high learning rate regime, the training behavior is not convex-like, but training longer in this period achieves better generalization. This thesis focuses on rethinking deep network training from the perspective of these phases in training. Empirical results suggest that each training regime, although distinct, work together to produce high performance on deep learning tasks. Moreover, we re-examine popular learning rate schedules and find that the paradigm of high and low learning rate regimes helps to explain their advantages.

Rethinking Continual Learning Approach and Study Out-of-distribution Generalization Algorithms

2023

Author : Touraj Laleh
Release : 2023
Genre :
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Book Synopsis Rethinking Continual Learning Approach and Study Out-of-distribution Generalization Algorithms by : Touraj Laleh

Download or read book Rethinking Continual Learning Approach and Study Out-of-distribution Generalization Algorithms written by Touraj Laleh. This book was released on 2023. Available in PDF, EPUB and Kindle. Book excerpt: One of the challenges of current machine learning systems is that standard AI paradigms are not good at transferring (or leveraging) knowledge across tasks. While many systems have been trained and achieved high performance on a specific distribution of a task, it is not easy to train AI systems that can perform well on a diverse set of tasks that belong to different distributions. This problem has been addressed from different perspectives in different domains including continual learning and out-of-distribution generalization. If an AI system is trained on a set of tasks belonging to different distributions, it could forget the knowledge it acquired from previous tasks. In continual learning, this process results in catastrophic forgetting which is one of the core issues of this domain. The first research project in this thesis focuses on the comparison of a chaotic learner and a naive continual learning setup. Training a deep neural network model usually requires multiple iterations, or epochs, over the training data set, to better estimate the parameters of the model. Most proposed approaches for this issue try to compensate for the effects of parameter updates in the batch incremental setup in which the training model visits a lot of samples for several epochs. However, it is not realistic to expect training data will always be fed to the model. In this chapter, we propose a chaotic stream learner that mimics the chaotic behavior of biological neurons and does not update network parameters. In addition, it can work with fewer samples compared to deep learning models on stream learning setups. Interestingly, our experiments on different datasets show that the chaotic stream learner has less catastrophic forgetting by its nature in comparison to a CNN model in continual learning. Deep Learning models have a naive out-of-distribution~(OoD) generalization performance where the testing distribution is unknown and different from the training. In the last years, there have been many research projects to compare OoD algorithms, including average and score-based methods. However, most proposed methods do not consider the level of difficulty of tasks. The second research project in this thesis, analysis some logical and practical strengths and drawbacks of existing methods for comparing and ranking OoD algorithms. We propose a novel ranking approach to define the task difficulty ratios to compare OoD generalization algorithms. We compared the average, score-based, and difficulty-based rankings of four selected tasks from the WILDS benchmark and five popular OoD algorithms for the experiment. The analysis shows significant changes in the ranking orders compared with current ranking approaches.

Deep Learning Quick Reference

2018-03-09 Computers

Author : Michael Bernico
Release : 2018-03-09
Genre : Computers
Kind : eBook
Book Rating : 912/5 ( reviews)

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Book Synopsis Deep Learning Quick Reference by : Michael Bernico

Download or read book Deep Learning Quick Reference written by Michael Bernico. This book was released on 2018-03-09. Available in PDF, EPUB and Kindle. Book excerpt: Dive deeper into neural networks and get your models trained, optimized with this quick reference guide Key Features A quick reference to all important deep learning concepts and their implementations Essential tips, tricks, and hacks to train a variety of deep learning models such as CNNs, RNNs, LSTMs, and more Supplemented with essential mathematics and theory, every chapter provides best practices and safe choices for training and fine-tuning your models in Keras and Tensorflow. Book Description Deep learning has become an essential necessity to enter the world of artificial intelligence. With this book deep learning techniques will become more accessible, practical, and relevant to practicing data scientists. It moves deep learning from academia to the real world through practical examples. You will learn how Tensor Board is used to monitor the training of deep neural networks and solve binary classification problems using deep learning. Readers will then learn to optimize hyperparameters in their deep learning models. The book then takes the readers through the practical implementation of training CNN's, RNN's, and LSTM's with word embeddings and seq2seq models from scratch. Later the book explores advanced topics such as Deep Q Network to solve an autonomous agent problem and how to use two adversarial networks to generate artificial images that appear real. For implementation purposes, we look at popular Python-based deep learning frameworks such as Keras and Tensorflow, Each chapter provides best practices and safe choices to help readers make the right decision while training deep neural networks. By the end of this book, you will be able to solve real-world problems quickly with deep neural networks. What you will learn Solve regression and classification challenges with TensorFlow and Keras Learn to use Tensor Board for monitoring neural networks and its training Optimize hyperparameters and safe choices/best practices Build CNN's, RNN's, and LSTM's and using word embedding from scratch Build and train seq2seq models for machine translation and chat applications. Understanding Deep Q networks and how to use one to solve an autonomous agent problem. Explore Deep Q Network and address autonomous agent challenges. Who this book is for If you are a Data Scientist or a Machine Learning expert, then this book is a very useful read in training your advanced machine learning and deep learning models. You can also refer this book if you are stuck in-between the neural network modeling and need immediate assistance in getting accomplishing the task smoothly. Some prior knowledge of Python and tight hold on the basics of machine learning is required.

Recapture the Rapture

2021-04-27 Psychology

Author : Jamie Wheal
Release : 2021-04-27
Genre : Psychology
Kind : eBook
Book Rating : 49X/5 ( reviews)

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Book Synopsis Recapture the Rapture by : Jamie Wheal

Download or read book Recapture the Rapture written by Jamie Wheal. This book was released on 2021-04-27. Available in PDF, EPUB and Kindle. Book excerpt: “A highly personal, richly informed and culturally wide-ranging meditation on the loss of meaning in our times and on pathways to rediscovering it.” —Gabor Maté, MD, author of In The Realm of Hungry Ghosts: Close Encounters With Addiction A neuroanthropologist maps out a revolutionary new practice—Hedonic Engineering—that combines the best of neuroscience and optimal psychology. It’s an intensive program of breathing, movement, and sexuality that mends trauma, heightens inspiration and tightens connections—helping us wake up, grow up, and show up for a world that needs us all. This is a book about a big idea. And the idea is this: Slowly over the past few decades, and now suddenly, all at once, we’re suffering from a collapse in Meaning. Fundamentalism and nihilism are filling that vacuum, with consequences that affect us all. In a world that needs us at our best, diseases of despair, tribalism, and disaster fatigue are leaving us at our worst. It’s vital that we regain control of the stories we’re telling because they are shaping the future we’re creating. To do that, we have to remember our deepest inspiration, heal our pain and apathy, and connect to each other like never before. If we can do that, we’ve got a shot at solving the big problems we face. And if we can’t? Well, the dustbin of history has swallowed civilizations older and fancier than ours. This book is divided into three parts. The first, Choose Your Own Apocalypse, takes a look at our current Meaning Crisis--where we are today, why it’s so hard to make sense of the world, what might be coming next, and what to do about it. It also makes a case that many of our efforts to cope, whether anxiety and denial, or tribalism and identity politics, are likely making things worse. The middle section, The Alchemist Cookbook, applies the creative firm IDEO’s design thinking to the Meaning Crisis. This is where the book gets hands on--taking a look at the strongest evolutionary drivers that can bring about inspiration, healing, and connection. From breathing, to movement, sexuality, music, and substances--these are the everyday tools to help us wake up, grow up, and show up. AKA--how to blow yourself sky high with household materials. And the best part? They’re accessible, by anyone anywhere, no middleman required. Transcendence democratized. The final third of the book, Ethical Cult Building, focuses on the tricky nature of putting these kinds of experiences into gear and into culture—because, anytime in the past when we’ve figured out combinations of peak states and deep healing, we’ve almost always ended up with problematic culty communities. Playing with fire has left a lot of people burned. This section lays out a roadmap for sparking a thousand fires around the world--each one unique and tailored to the needs and values of its participants. Think of it as an open-source toolkit for building ethical culture. In Recapture the Rapture, we’re taking radical research out of the extremes and applying it to the mainstream--to the broader social problem of healing, believing, and belonging. It’s providing answers to the questions we face: how to replace blind faith with direct experience, how to move from broken to whole, and how to cure isolation with connection. Said even more plainly, it shows us how to revitalize our bodies, boost our creativity, rekindle our relationships, and answer once and for all the questions of why we are here and what do we do now? In a world that needs the best of us from the rest of us, this is a book that shows us how to get it done.

Strengthening Deep Neural Networks

2019-07-03 Computers

Author : Katy Warr
Release : 2019-07-03
Genre : Computers
Kind : eBook
Book Rating : 903/5 ( reviews)

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Book Synopsis Strengthening Deep Neural Networks by : Katy Warr

Download or read book Strengthening Deep Neural Networks written by Katy Warr. This book was released on 2019-07-03. Available in PDF, EPUB and Kindle. Book excerpt: As deep neural networks (DNNs) become increasingly common in real-world applications, the potential to deliberately "fool" them with data that wouldn’t trick a human presents a new attack vector. This practical book examines real-world scenarios where DNNs—the algorithms intrinsic to much of AI—are used daily to process image, audio, and video data. Author Katy Warr considers attack motivations, the risks posed by this adversarial input, and methods for increasing AI robustness to these attacks. If you’re a data scientist developing DNN algorithms, a security architect interested in how to make AI systems more resilient to attack, or someone fascinated by the differences between artificial and biological perception, this book is for you. Delve into DNNs and discover how they could be tricked by adversarial input Investigate methods used to generate adversarial input capable of fooling DNNs Explore real-world scenarios and model the adversarial threat Evaluate neural network robustness; learn methods to increase resilience of AI systems to adversarial data Examine some ways in which AI might become better at mimicking human perception in years to come

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Rethinking Methods to Train Deep Neural Networks

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