Download or read book Leveraging Domain Knowledge in Deep Learning Systems written by Colin M. Van Oort and published by . This book was released on 2021 with total page 356 pages. Available in PDF, EPUB and Kindle. Book excerpt: Machine learning, and the sub-field of deep learning in particular, has experienced an explosion in research interest and practical applications over the past few decades. Deep learning approaches seem to have become the preferred approach in many domains, outpacing the use of more traditional machine learning methods. This transition has also coincided with a shift away from feature engineering based on domain knowledge. Instead, the common deep learning philosophy is to learn relevant features through the combination of expressive models and large datasets. Some have interpreted this paradigm shift as the death of domain knowledge. I argue that domain knowledge is still broadly used in deep learning systems, and even critically important, but where and how domain knowledge is used has evolved. To support this argument I present three recent deep learning applications in disparate domains that each heavily rely on domain knowledge. Based on these three applications I discuss strategies for where and how domain knowledge is being effectively incorporated into newer deep learning systems.

Download or read book Deep Learning for Unstructured Data by Leveraging Domain Knowledge written by Shanshan Zhang and published by . This book was released on 2019 with total page 113 pages. Available in PDF, EPUB and Kindle. Book excerpt: Unstructured data such as texts, strings, images, audios, videos are everywhere due to the social interaction on the Internet and the high-throughput technology in sciences, e.g., chemistry and biology. However, for traditional machine learning algorithms, classifying a text document is far more difficult than classifying a data entry in a spreadsheet. We have to convert the unstructured data into some numeric vectors which can then be understood by machine learning algorithms. For example, a sentence is first converted to a vector of word counts, and then fed into a classification algorithm such as logistic regression and support vector machine. The creation of such numerical vectors is very challenging and difficult. Recent progress in deep learning provides us a new way to jointly learn features and train classifiers for unstructured data. For example, recurrent neural networks proved successful at learning from a sequence of word indices; convolutional neural networks are effective to learn from videos, which are sequences of pixel matrices. Our research focuses on developing novel deep learning approaches for text and graph data. Breakthroughs using deep learning have been made during the last few years for many core tasks in natural language processing, such as machine translation, POS tagging, named entity recognition, etc. However, when it comes to informal and noisy text data, such as tweets, HTMLs, OCR, there are two major issues with modern deep learning technologies. First, deep learning requires large amount of labeled data to train an effective model; second, neural network architectures that work with natural language are not proper with informal text. In this thesis, we address the two important issues and develop new deep learning approaches in four supervised and unsupervised tasks with noisy text. We first present a deep feature engineering approach for informative tweets discovery during the emerging disasters. We propose to use unlabeled microblogs to cluster words into a limited number of clusters and use the word clusters as features for tweets discovery. Our results indicate that when the number of labeled tweets is 100 or less, the proposed approach is superior to the standard classification based on the bag or words feature representation. We then introduce a human-in-the-loop (HIL) framework for entity identification from noisy web text. Our work explores ways to combine the expressive power of REs, ability of deep learning to learn from large data into a new integrated framework for entity identification from web data. The evaluation on several entity identification problems shows that the proposed framework achieves very high accuracy while requiring only a modest human involvement. We further extend the framework of entity identification to an iterative HIL framework that addresses the entity recognition problem. We particularly investigate how human invest their time when a user is allowed to choose between regex construction and manual labeling. Finally, we address a fundamental problem in the text mining domain, i.e, embedding of rare and out-of-vocabulary (OOV) words, by refining word embedding models and character embedding models in an iterative way. We illustrate the simplicity but effectiveness of our method when applying it to online professional profiles allowing noisy user input. Graph neural networks have been shown great success in the domain of drug design and material sciences, where organic molecules and crystal structures of materials are represented as attributed graphs. A deep learning architecture that is capable of learning from graph nodes and graph edges is crucial for property estimation of molecules. In this dissertation, We propose a simple graph representation for molecules and three neural network architectures that is able to directly learn predictive functions from graphs. We discover that, it is true graph networks are superior than feature-driven algorithms for formation energy prediction. However, the superiority can not be reproduced on band gap prediction. We also discovered that our proposed simple shallow neural networks perform comparably with the state-of-the-art deep neural networks.

Download or read book Two Topics in Deep Learning Based Dialogue Systems written by Jinglun Cai and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Building more intelligent and more resourceful dialogue systems has been a long sought-after goal in Artificial Intelligence and Natural Language Processing. In this dissertation, we investigate two topics in deep learning based dialogue systems: Leveraging Knowledge Graph (KG) in Query Rewriting (QR), and balancing multi-domain corpora in response generation. Query rewriting is a critical component in dialogue systems to reduce frictions caused by systematic errors or user ambiguity. When there is an entity error, it imposes extra difficulties for a dialogue system to produce satisfactory interpretation. Knowledge Graph information can be beneficial for query rewriting, as common queries for dialogue systems are often related to external knowledge like celebrity names, artworks names, and locations. In this dissertation, we investigate how to incorporate knowledge graph for query rewriting. In particular, we study knowledge graph based entity retrieval in query rewriting. We utilize neural encoders to produce utterance/entity embeddings, and similarity search for entity retrieval. We incorporate KG to provide graph structural information (neighboring entities in KG encoded by Graph Neural Network) and textual information (KG entity descriptions encoded by RoBERTa). Experimental results show that both hard negative sampling and incorporation of knowledge graph consistently improve retrieval precision on all test sets. We observe large performance gain from utilizing KG on few-shot test entities. This shows that external knowledge information is particularly beneficial for dealing with rarely seen cases. Open-domain conversational systems are assumed to generate good responses on multiple domains. Previous work achieved good performance on a single corpus, but combining multiple corpora from different domains is less studied. In this dissertation, we explore methods that deal with multi-domain corpora. We first investigate interleaved learning which intermingles multiple corpora as the baseline. We then investigate two multi-domain learning methods, labeled learning and multi-task labeled learning, which encode each corpus through a unique embedding. Furthermore, we propose Domain-specific Frequency (DF), a novel word-level importance weight that measures the relative importance of a word in a specific corpus compared to multiple corpora. Based on DF, we propose weighted learning, a method that integrates DF to the loss function, and we also adopt DF as a new evaluation metric. Extensive experiments show that our methods gain significant improvements on both automatic and human evaluation. We share our code and data for reproducibility. Our main contributions are the following: 1. We propose a knowledge graph enhanced system for query rewriting, leveraging both graph neighbor information and entity textual description. 2. We propose labeled learning and multi-task labeled learning for open domain, and show these two methods achieve significant improvements on automatic and human evaluation.

Download or read book Deep Learning for Natural Language Processing written by Karthiek Reddy Bokka and published by Packt Publishing Ltd. This book was released on 2019-06-11 with total page 372 pages. Available in PDF, EPUB and Kindle. Book excerpt: Gain the knowledge of various deep neural network architectures and their application areas to conquer your NLP issues. Key FeaturesGain insights into the basic building blocks of natural language processingLearn how to select the best deep neural network to solve your NLP problemsExplore convolutional and recurrent neural networks and long short-term memory networksBook Description Applying deep learning approaches to various NLP tasks can take your computational algorithms to a completely new level in terms of speed and accuracy. Deep Learning for Natural Language Processing starts off by highlighting the basic building blocks of the natural language processing domain. The book goes on to introduce the problems that you can solve using state-of-the-art neural network models. After this, delving into the various neural network architectures and their specific areas of application will help you to understand how to select the best model to suit your needs. As you advance through this deep learning book, you’ll study convolutional, recurrent, and recursive neural networks, in addition to covering long short-term memory networks (LSTM). Understanding these networks will help you to implement their models using Keras. In the later chapters, you will be able to develop a trigger word detection application using NLP techniques such as attention model and beam search. By the end of this book, you will not only have sound knowledge of natural language processing but also be able to select the best text pre-processing and neural network models to solve a number of NLP issues. What you will learnUnderstand various pre-processing techniques for deep learning problemsBuild a vector representation of text using word2vec and GloVeCreate a named entity recognizer and parts-of-speech tagger with Apache OpenNLPBuild a machine translation model in KerasDevelop a text generation application using LSTMBuild a trigger word detection application using an attention modelWho this book is for If you’re an aspiring data scientist looking for an introduction to deep learning in the NLP domain, this is just the book for you. Strong working knowledge of Python, linear algebra, and machine learning is a must.

Download or read book Leveraging Prior Knowledge and Structure for Data efficient Machine Learning written by Beliz Gunel and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Building high-performing end-to-end machine learning systems primarily consists of developing the machine learning model and gathering high-quality training data for the application of interest, assuming one has access to the right hardware. Although machine learning models are getting increasingly commoditized in the last few years with the rise of open-sourced platforms, curating high-quality labeled training datasets is still either costly or not feasible for many real-world applications. Hence, we mainly focus on data in this thesis, specifically how to (1) reduce dependence on labeled data with data-efficient machine learning methods through either injecting domain-specific prior knowledge or leveraging existing software systems and datasets that have initially been created for different tasks, (2) effectively manage training data and build associated tooling in order to maximize the utility of the data, and (3) improve the quality of the data representations achieved by embeddings by matching the structure of the data to the geometry of the embedding space. We start by describing our works on building data-efficient machine learning methods for accelerated magnetic resonance imaging (MRI) reconstruction through physics-driven augmentations for consistency training, scale-equivariant unrolled neural networks, and weak supervision using untrained neural networks. Then, we describe our works on building data-efficient machine learning methods for natural language understanding. In particular, we discuss a supervised contrastive learning approach for pre-trained language model fine-tuning and a large-scale data augmentation method to retrieve in-domain data. Related to effectively managing training data, we discuss our proposed information extraction system for form-like documents Glean and focus on the often overlooked aspects of training data management and associated tooling. We highlight the importance of effectively managing training data by showing that it is at least as critical as the machine learning model advances in terms of downstream extraction performance on a real-world dataset. Finally, to improve embedding representations for a variety of types of data, we investigate spaces with heterogeneous curvature. We demonstrate mixed-curvature representations provide higher quality representations both for graphs and for word embeddings. Also, we investigate integrating entity embeddings from Wikidata knowledge graph to an abstractive text summarization model to enhance factuality.

Download or read book Towards Green AI written by Sangeeta Srivastava (Researcher in deep learning) and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Deep neural networks have not only evolved to produce state-of-the-art artificial intelligence (AI) models, but also generalize well, especially when the network has a large capacity and access to a comparable massive training dataset. These models are computationally and environmentally inefficient. Such research works are known as Red AI. However, it is challenging to develop algorithms that generate cost-effective models for Green AI without sacrificing predictive strength or generality. This dissertation shows how we can effectively integrate domain knowledge, either implicitly or explicitly, into an end-to-end learning pipeline to reduce the training and testing overhead of a neural network. In particular, we exploit the target data and task information to make two types of modifications in the learning pipeline, each of which helps to lower the neural network's run-time costs in unique ways. We re-define the learning task as one or more "simpler" subtask(s) so that the subtask(s) requires fewer parameters and training data. Explicit inclusion of a regularization term in the objective function allows us to limit the exploration space to those that are both relevant and plausible for a given application. We demonstrate the benefits of the above methodologies with the help of three applications with disparate challenges: 1) acoustic event detection, 2) radar classification, and 3) scientific problems based on eigenvalue solvers. Acoustic and radar applications require on-device intelligence and rely on Cortex-M7/M3 microcontroller-based edge devices with limited hardware resources and a low power budget. On the other hand, eigenvalue problems yield memory- and compute-intensive models primarily due to the size of the output layer that grows exponentially with the number of particles in the system. For the acoustic event detector, we exploit unlabeled data from the target domain to constrain knowledge transfer from a large "teacher" model to a smaller "student" model such that the knowledge is relevant to the target problem. This results in a student with orders of magnitude lower static and dynamic memory requirements. For the N + 1 radar classification problem, where the +1-class corresponds to environmental noise, we use an application-specific ontology to decompose the end-to-end learning into two hierarchical subproblems. A more complex model trained to classify the N source classes is invoked occasionally, i.e., only when there is no environmental noise. This conditional invocation makes the resulting model 3.5x more run-time efficient than feature-engineered shallow solutions and gives the best trade-off between accuracy and efficiency. As for the eigenvalue solvers, we decompose the complex regression task of prediction of high-dimensional eigenvector into multiple simpler and parallel subtasks such that the inputs in each subtask have similar physical properties. We learn a parameter-efficient "expert" network for each subtask. Our proposed physics-guided architecture is 150x smaller than the network trained to learn the complex task while being competitive in generalization. Our results also show that domain knowledge helps reduce the amount of supervised data needed for model training.

Download or read book Deep Learning Essentials written by Anurag Bhardwaj and published by Packt Publishing Ltd. This book was released on 2018-01-30 with total page 271 pages. Available in PDF, EPUB and Kindle. Book excerpt: Get to grips with the essentials of deep learning by leveraging the power of Python Key Features Your one-stop solution to get started with the essentials of deep learning and neural network modeling Train different kinds of neural networks to tackle various problems in Natural Language Processing, computer vision, speech recognition, and more Covers popular Python libraries such as Tensorflow, Keras, and more, along with tips on training, deploying and optimizing your deep learning models in the best possible manner Book Description Deep Learning a trending topic in the field of Artificial Intelligence today and can be considered to be an advanced form of machine learning, which is quite tricky to master. This book will help you take your first steps in training efficient deep learning models and applying them in various practical scenarios. You will model, train, and deploy different kinds of neural networks such as Convolutional Neural Network, Recurrent Neural Network, and will see some of their applications in real-world domains including computer vision, natural language processing, speech recognition, and so on. You will build practical projects such as chatbots, implement reinforcement learning to build smart games, and develop expert systems for image captioning and processing. Popular Python library such as TensorFlow is used in this book to build the models. This book also covers solutions for different problems you might come across while training models, such as noisy datasets, small datasets, and more. This book does not assume any prior knowledge of deep learning. By the end of this book, you will have a firm understanding of the basics of deep learning and neural network modeling, along with their practical applications. What you will learn Get to grips with the core concepts of deep learning and neural networks Set up deep learning library such as TensorFlow Fine-tune your deep learning models for NLP and Computer Vision applications Unify different information sources, such as images, text, and speech through deep learning Optimize and fine-tune your deep learning models for better performance Train a deep reinforcement learning model that plays a game better than humans Learn how to make your models get the best out of your GPU or CPU Who this book is for Aspiring data scientists and machine learning experts who have limited or no exposure to deep learning will find this book to be very useful. If you are looking for a resource that gets you up and running with the fundamentals of deep learning and neural networks, this book is for you. As the models in the book are trained using the popular Python-based libraries such as Tensorflow and Keras, it would be useful to have sound programming knowledge of Python.

Download or read book Artificial Intelligence and Deep Learning Essentials written by James Russell and published by Independently Published. This book was released on 2018-05-12 with total page 142 pages. Available in PDF, EPUB and Kindle. Book excerpt: Get to grips with the essentials of deep learning by leveraging the power of PythonKey Features Your one-stop solution to get started with the essentials of deep learning and neural network modeling Train different kinds of neural networks to tackle various problems in Natural Language Processing, computer vision, speech recognition, and more Covers popular Python libraries such as Tensorflow, Keras, and more, along with tips on training, deploying and optimizing your deep learning models in the best possible manner Deep Learning a trending topic in the field of Artificial Intelligence today and can be considered to be an advanced form of machine learning, which is quite tricky to master. This book will help you take your first steps in training efficient deep learning models and applying them in various practical scenarios. You will model, train, and deploy different kinds of neural networks such as Convolutional Neural Network, Recurrent Neural Network, and will see some of their applications in real-world domains including computer vision, natural language processing, speech recognition, and so on. You will build practical projects such as chatbots, implement reinforcement learning to build smart games, and develop expert systems for image captioning and processing. Popular Python library such as TensorFlow is used in this book to build the models. This book also covers solutions for different problems you might come across while training models, such as noisy datasets, small datasets, and more. This book does not assume any prior knowledge of deep learning. By the end of this book, you will have a firm understanding of the basics of deep learning and neural network modeling, along with their practical applications.What you will learn Get to grips with the core concepts of deep learning and neural networks Set up deep learning library such as TensorFlow Fine-tune your deep learning models for NLP and Computer Vision applications Unify different information sources, such as images, text, and speech through deep learning Optimize and fine-tune your deep learning models for better performance Train a deep reinforcement learning model that plays a game better than humans Learn how to make your models get the best out of your GPU or CPU Who This Book Is For Aspiring data scientists and machine learning experts who have limited or no exposure to deep learning will find this book to be very useful. If you are looking for a resource that gets you up and running with the fundamentals of deep learning and neural networks, this book is for you. As the models in the book are trained using the popular Python-based libraries such as Tensorflow and Keras, it would be useful to have sound programming knowledge of Python. Table of Contents 1. What is artificial intelligence 2. Why is the artificial intelligence important ? 3. Applications of Machine Learning 4. Semantics, Probability and IA 5. Numerical Computation 6. Sequence Modeling, Recurrent and Recursive Nets 7. Autoencoders 8. Markov Chains, Monte Carlo Methods, and Machine Learning

Download or read book Domain Adaptation in Computer Vision with Deep Learning written by Hemanth Venkateswara and published by Springer Nature. This book was released on 2020-08-18 with total page 256 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides a survey of deep learning approaches to domain adaptation in computer vision. It gives the reader an overview of the state-of-the-art research in deep learning based domain adaptation. This book also discusses the various approaches to deep learning based domain adaptation in recent years. It outlines the importance of domain adaptation for the advancement of computer vision, consolidates the research in the area and provides the reader with promising directions for future research in domain adaptation. Divided into four parts, the first part of this book begins with an introduction to domain adaptation, which outlines the problem statement, the role of domain adaptation and the motivation for research in this area. It includes a chapter outlining pre-deep learning era domain adaptation techniques. The second part of this book highlights feature alignment based approaches to domain adaptation. The third part of this book outlines image alignment procedures for domain adaptation. The final section of this book presents novel directions for research in domain adaptation. This book targets researchers working in artificial intelligence, machine learning, deep learning and computer vision. Industry professionals and entrepreneurs seeking to adopt deep learning into their applications will also be interested in this book.

Download or read book Reinforcement Learning with TensorFlow written by Sayon Dutta and published by Packt Publishing Ltd. This book was released on 2018-04-24 with total page 327 pages. Available in PDF, EPUB and Kindle. Book excerpt: Leverage the power of the Reinforcement Learning techniques to develop self-learning systems using Tensorflow Key Features Learn reinforcement learning concepts and their implementation using TensorFlow Discover different problem-solving methods for Reinforcement Learning Apply reinforcement learning for autonomous driving cars, robobrokers, and more Book Description Reinforcement Learning (RL), allows you to develop smart, quick and self-learning systems in your business surroundings. It is an effective method to train your learning agents and solve a variety of problems in Artificial Intelligence—from games, self-driving cars and robots to enterprise applications that range from datacenter energy saving (cooling data centers) to smart warehousing solutions. The book covers the major advancements and successes achieved in deep reinforcement learning by synergizing deep neural network architectures with reinforcement learning. The book also introduces readers to the concept of Reinforcement Learning, its advantages and why it’s gaining so much popularity. The book also discusses on MDPs, Monte Carlo tree searches, dynamic programming such as policy and value iteration, temporal difference learning such as Q-learning and SARSA. You will use TensorFlow and OpenAI Gym to build simple neural network models that learn from their own actions. You will also see how reinforcement learning algorithms play a role in games, image processing and NLP. By the end of this book, you will have a firm understanding of what reinforcement learning is and how to put your knowledge to practical use by leveraging the power of TensorFlow and OpenAI Gym. What you will learn Implement state-of-the-art Reinforcement Learning algorithms from the basics Discover various techniques of Reinforcement Learning such as MDP, Q Learning and more Learn the applications of Reinforcement Learning in advertisement, image processing, and NLP Teach a Reinforcement Learning model to play a game using TensorFlow and the OpenAI gym Understand how Reinforcement Learning Applications are used in robotics Who this book is for If you want to get started with reinforcement learning using TensorFlow in the most practical way, this book will be a useful resource. The book assumes prior knowledge of machine learning and neural network programming concepts, as well as some understanding of the TensorFlow framework. No previous experience with Reinforcement Learning is required.

Download or read book Introduction to Transfer Learning written by Jindong Wang and published by Springer Nature. This book was released on 2023-03-30 with total page 333 pages. Available in PDF, EPUB and Kindle. Book excerpt: Transfer learning is one of the most important technologies in the era of artificial intelligence and deep learning. It seeks to leverage existing knowledge by transferring it to another, new domain. Over the years, a number of relevant topics have attracted the interest of the research and application community: transfer learning, pre-training and fine-tuning, domain adaptation, domain generalization, and meta-learning. This book offers a comprehensive tutorial on an overview of transfer learning, introducing new researchers in this area to both classic and more recent algorithms. Most importantly, it takes a “student’s” perspective to introduce all the concepts, theories, algorithms, and applications, allowing readers to quickly and easily enter this area. Accompanying the book, detailed code implementations are provided to better illustrate the core ideas of several important algorithms, presenting good examples for practice.

Download or read book The Deep Learning AI Playbook written by Carlos Perez and published by Lulu.com. This book was released on 2017-10-11 with total page 352 pages. Available in PDF, EPUB and Kindle. Book excerpt: Just like any new technology, what perplexes many is the question of how to apply Deep Learning in a business context. Technology that is disruptive does not automatically imply that the development of valuable use cases are apparent. For years, many people could not figure out how to monetize the World Wide Web. We are in that same situation with Deep Learning AI. The developments are mind-boggling but the monetization is far from being obvious.Deep Learning Artificial Intelligence involves the interplay of Computer Science, Physics, Biology, Linguistics and Psychology. In addition to that, it is technology that can be extremely disruptive. Furthermore, the ramifications to society and even our own humanity can be immense. There are few subjects that are as captivating and as consequential as this. Surprisingly, there is very little that is written about this new technology in a more comprehensive and cohesive way. This book is an opinionated take on the developments of Deep Learning AI.

Download or read book Hands On Computer Vision with TensorFlow 2 written by Benjamin Planche and published by Packt Publishing Ltd. This book was released on 2019-05-30 with total page 363 pages. Available in PDF, EPUB and Kindle. Book excerpt: A practical guide to building high performance systems for object detection, segmentation, video processing, smartphone applications, and more Key FeaturesDiscover how to build, train, and serve your own deep neural networks with TensorFlow 2 and KerasApply modern solutions to a wide range of applications such as object detection and video analysisLearn how to run your models on mobile devices and web pages and improve their performanceBook Description Computer vision solutions are becoming increasingly common, making their way into fields such as health, automobile, social media, and robotics. This book will help you explore TensorFlow 2, the brand new version of Google's open source framework for machine learning. You will understand how to benefit from using convolutional neural networks (CNNs) for visual tasks. Hands-On Computer Vision with TensorFlow 2 starts with the fundamentals of computer vision and deep learning, teaching you how to build a neural network from scratch. You will discover the features that have made TensorFlow the most widely used AI library, along with its intuitive Keras interface. You'll then move on to building, training, and deploying CNNs efficiently. Complete with concrete code examples, the book demonstrates how to classify images with modern solutions, such as Inception and ResNet, and extract specific content using You Only Look Once (YOLO), Mask R-CNN, and U-Net. You will also build generative adversarial networks (GANs) and variational autoencoders (VAEs) to create and edit images, and long short-term memory networks (LSTMs) to analyze videos. In the process, you will acquire advanced insights into transfer learning, data augmentation, domain adaptation, and mobile and web deployment, among other key concepts. By the end of the book, you will have both the theoretical understanding and practical skills to solve advanced computer vision problems with TensorFlow 2.0. What you will learnCreate your own neural networks from scratchClassify images with modern architectures including Inception and ResNetDetect and segment objects in images with YOLO, Mask R-CNN, and U-NetTackle problems faced when developing self-driving cars and facial emotion recognition systemsBoost your application's performance with transfer learning, GANs, and domain adaptationUse recurrent neural networks (RNNs) for video analysisOptimize and deploy your networks on mobile devices and in the browserWho this book is for If you're new to deep learning and have some background in Python programming and image processing, like reading/writing image files and editing pixels, this book is for you. Even if you're an expert curious about the new TensorFlow 2 features, you'll find this book useful. While some theoretical concepts require knowledge of algebra and calculus, the book covers concrete examples focused on practical applications such as visual recognition for self-driving cars and smartphone apps.

Download or read book Deep Learning in Smart eHealth Systems written by Asma Channa and published by Springer Nature. This book was released on 2023-12-07 with total page 102 pages. Available in PDF, EPUB and Kindle. Book excerpt: One of the main benefits of this book is that it presents a comprehensive and innovative eHealth framework that leverages deep learning and IoT wearable devices for the evaluation of Parkinson's disease patients. This framework offers a new way to assess and monitor patients' motor deficits in a personalized and automated way, improving the efficiency and accuracy of diagnosis and treatment. Compared to other books on eHealth and Parkinson's disease, this book offers a unique perspective and solution to the challenges facing patients and healthcare providers. It combines state-of-the-art technology, such as wearable devices and deep learning algorithms, with clinical expertise to develop a personalized and efficient evaluation framework for Parkinson's disease patients. This book provides a roadmap for the integration of cutting-edge technology into clinical practice, paving the way for more effective and patient-centered healthcare. To understand this book, readers should have a basic knowledge of eHealth, IoT, deep learning, and Parkinson's disease. However, the book provides clear explanations and examples to make the content accessible to a wider audience, including researchers, practitioners, and students interested in the intersection of technology and healthcare.

Download or read book Deep Learning at Scale written by Suneeta Mall and published by "O'Reilly Media, Inc.". This book was released on 2024-06-18 with total page 404 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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

Download or read book Fundamentals of Deep Learning written by Nikhil Buduma and published by "O'Reilly Media, Inc.". This book was released on 2022-05-16 with total page 390 pages. Available in PDF, EPUB and Kindle. Book excerpt: We're in the midst of an AI research explosion. Deep learning has unlocked superhuman perception to power our push toward creating self-driving vehicles, defeating human experts at a variety of difficult games including Go, and even generating essays with shockingly coherent prose. But deciphering these breakthroughs often takes a PhD in machine learning and mathematics. The updated second edition of this book describes the intuition behind these innovations without jargon or complexity. Python-proficient programmers, software engineering professionals, and computer science majors will be able to reimplement these breakthroughs on their own and reason about them with a level of sophistication that rivals some of the best developers in the field. Learn the mathematics behind machine learning jargon Examine the foundations of machine learning and neural networks Manage problems that arise as you begin to make networks deeper Build neural networks that analyze complex images Perform effective dimensionality reduction using autoencoders Dive deep into sequence analysis to examine language Explore methods in interpreting complex machine learning models Gain theoretical and practical knowledge on generative modeling Understand the fundamentals of reinforcement learning

Download or read book Mastering Deep Learning written by Cybellium Ltd and published by Cybellium Ltd. This book was released on with total page 240 pages. Available in PDF, EPUB and Kindle. Book excerpt: Unleash the Power of Neural Networks for Intelligent Solutions In the landscape of artificial intelligence and machine learning, deep learning stands as a revolutionary force that is shaping the future of technology. "Mastering Deep Learning" is your ultimate guide to comprehending and harnessing the potential of deep neural networks, empowering you to create intelligent solutions that drive innovation. About the Book: As the capabilities of technology expand, deep learning emerges as a transformative approach that unlocks the potential of artificial intelligence. "Mastering Deep Learning" offers a comprehensive exploration of this cutting-edge field—an indispensable toolkit for data scientists, engineers, and enthusiasts. This book caters to both beginners and experienced learners aiming to excel in deep learning concepts, algorithms, and applications. Key Features: Deep Learning Fundamentals: Begin by understanding the core principles of deep learning. Learn about neural networks, activation functions, and backpropagation—the building blocks of the subject. Deep Neural Architectures: Dive into the world of deep neural architectures. Explore techniques for building and designing different types of neural networks, including feedforward, convolutional, and recurrent networks. Training and Optimization: Grasp the art of training deep neural networks. Understand techniques for weight initialization, gradient descent, and optimization algorithms to ensure efficient learning. Natural Language Processing: Explore deep learning applications in natural language processing. Learn how to process and understand text, sentiment analysis, and language generation. Computer Vision: Understand the significance of deep learning in computer vision. Explore techniques for image classification, object detection, and image generation. Reinforcement Learning: Delve into the realm of reinforcement learning. Explore techniques for training agents to interact with environments and make intelligent decisions. Transfer Learning and Pretrained Models: Grasp the power of transfer learning. Learn how to leverage pretrained models and adapt them to new tasks. Real-World Applications: Gain insights into how deep learning is applied across industries. From healthcare to finance, discover the diverse applications of deep neural networks. Why This Book Matters: In an era of rapid technological advancement, mastering deep learning offers a competitive edge. "Mastering Deep Learning" empowers data scientists, engineers, and technology enthusiasts to leverage these cutting-edge concepts, enabling them to create intelligent solutions that drive innovation and redefine possibilities. Unleash the Future of AI: In the landscape of artificial intelligence, deep learning is reshaping technology and innovation. "Mastering Deep Learning" equips you with the knowledge needed to leverage deep neural networks, enabling you to create intelligent solutions that push the boundaries of possibilities. Whether you're a seasoned practitioner or new to the world of deep learning, this book will guide you in building a solid foundation for effective AI-driven solutions. Your journey to mastering deep learning starts here. © 2023 Cybellium Ltd. All rights reserved. www.cybellium.com