Download or read book The Return into the Deep written by Kathleen Martin and published by Balboa Press. This book was released on 2022-12-30 with total page 277 pages. Available in PDF, EPUB and Kindle. Book excerpt: When Mike Aul receives an urgent summons from Arthur White Horse, asking for his help, he’s forced to weigh out the consequences. Still dealing with the nightmares from his last trip to the Navajo Indian Reservation, ten months earlier, he finds himself torn between answering Arthur’s urgent call, and keeping his sanity. But, considering Arthur a good friend, he decides to throw caution to the wind, and catches the first flight to Arizona. He thinks he’ll be building a goat pen. However, what Arthur has in mind is much more demanding, than pounding nails! When the young tattoo artist arrives at Arthur’s home on the “Rez”, he’s met with dire news. “Black Fox is missing!” Arthur tells him. Mike is taken aback! Black Fox is a name that lived only in 1830, and in his past life as Two Ponies. Not in 2017! In reality, the old Hopi shaman passed on nearly two centuries earlier! How can he now be considered missing? What happens next, sends Mike on an adventure that nearly costs him his life, and he’s forced to rethink everything he’s ever known about the world around him.

Download or read book Return of the Deep Ones and Other Mythos Tales written by Brian Lumley and published by Crossroad Press. This book was released on 2017-09-11 with total page 397 pages. Available in PDF, EPUB and Kindle. Book excerpt: Brian Lumley, author of the bestselling Necroscope and Vampire World series of novels, has for many years been a devotee' of H. P. Lovecraft’s Cthulhu Mythos, by such nightmare fables as Dagon, The Call of Cthulhu The Shadow Over Innsmouth, Lovecraft’s legendary Deep Ones have taken their place in terror fiction alongside the vampire and the werewolf. Now they are given the Lumley treatment in—RETURN OF THE DEEP ONES! But the Mythos was not restricted to tales of oceanic horror; nightmares out of space and time—and inner earth—abound in Lovecraft’s fiction. Thus, with the addition of Beneath the Moors, Inception, and the novella, Lord of the Worms, Brian Lumley offers his latest homage to Lovecraft the Master. Now, from forbidden depths of dream and ocean, the RETURN OF THE DEEP ONES! In the field of no-holds-barred terror fiction, there’s Brian Lumley—and then there’s the rest …

Download or read book The Deep written by Rivers Solomon and published by Simon and Schuster. This book was released on 2019-11-05 with total page 192 pages. Available in PDF, EPUB and Kindle. Book excerpt: Octavia E. Butler meets Marvel’s Black Panther in The Deep, a story rich with Afrofuturism, folklore, and the power of memory, inspired by the Hugo Award–nominated song “The Deep” from Daveed Diggs’s rap group Clipping. Yetu holds the memories for her people—water-dwelling descendants of pregnant African slave women thrown overboard by slave owners—who live idyllic lives in the deep. Their past, too traumatic to be remembered regularly is forgotten by everyone, save one—the historian. This demanding role has been bestowed on Yetu. Yetu remembers for everyone, and the memories, painful and wonderful, traumatic and terrible and miraculous, are destroying her. And so, she flees to the surface escaping the memories, the expectations, and the responsibilities—and discovers a world her people left behind long ago. Yetu will learn more than she ever expected about her own past—and about the future of her people. If they are all to survive, they’ll need to reclaim the memories, reclaim their identity—and own who they really are. The Deep is “a tour de force reorientation of the storytelling gaze…a superb, multilayered work,” (Publishers Weekly, starred review) and a vividly original and uniquely affecting story inspired by a song produced by the rap group Clipping.

Download or read book Python Deep Learning written by Valentino Zocca and published by Packt Publishing Ltd. This book was released on 2017-04-28 with total page 406 pages. Available in PDF, EPUB and Kindle. Book excerpt: Take your machine learning skills to the next level by mastering Deep Learning concepts and algorithms using Python. About This Book Explore and create intelligent systems using cutting-edge deep learning techniques Implement deep learning algorithms and work with revolutionary libraries in Python Get real-world examples and easy-to-follow tutorials on Theano, TensorFlow, H2O and more Who This Book Is For This book is for Data Science practitioners as well as aspirants who have a basic foundational understanding of Machine Learning concepts and some programming experience with Python. A mathematical background with a conceptual understanding of calculus and statistics is also desired. What You Will Learn Get a practical deep dive into deep learning algorithms Explore deep learning further with Theano, Caffe, Keras, and TensorFlow Learn about two of the most powerful techniques at the core of many practical deep learning implementations: Auto-Encoders and Restricted Boltzmann Machines Dive into Deep Belief Nets and Deep Neural Networks Discover more deep learning algorithms with Dropout and Convolutional Neural Networks Get to know device strategies so you can use deep learning algorithms and libraries in the real world In Detail With an increasing interest in AI around the world, deep learning has attracted a great deal of public attention. Every day, deep learning algorithms are used broadly across different industries. The book will give you all the practical information available on the subject, including the best practices, using real-world use cases. You will learn to recognize and extract information to increase predictive accuracy and optimize results. Starting with a quick recap of important machine learning concepts, the book will delve straight into deep learning principles using Sci-kit learn. Moving ahead, you will learn to use the latest open source libraries such as Theano, Keras, Google's TensorFlow, and H20. Use this guide to uncover the difficulties of pattern recognition, scaling data with greater accuracy and discussing deep learning algorithms and techniques. Whether you want to dive deeper into Deep Learning, or want to investigate how to get more out of this powerful technology, you'll find everything inside. Style and approach Python Machine Learning by example follows practical hands on approach. It walks you through the key elements of Python and its powerful machine learning libraries with the help of real world projects.

Download or read book Through Dungeons Deep written by Robert Plamondon and published by Norton Creek Press. This book was released on 2008-08-20 with total page 341 pages. Available in PDF, EPUB and Kindle. Book excerpt: Through Dungeons Deep delves into the art of role-playing, showing players and Game Masters how to have more fun and excitement with fantasy role-playing games. First published more than 25 years ago, this book was an instant classic. Long out of print, the original edition sells for several times its cover price. This Norton Creek Press reprint makes the book available (and affordable) again. Robert Plamondon wrote Through Dungeons Deep after realizing that the most important part of role-playing games-role-playing-is barely mentioned in gaming systems. When it is, it is often confused with rules. But role-playing really boils down to make-believe, and the real fun in role-playing games comes from unlocking your imagination. But it's also important to carry a length of rope and wear shoes you can run in.

Download or read book Spirit Deep written by Tisha M. Brooks and published by University of Virginia Press. This book was released on 2023-03-24 with total page 386 pages. Available in PDF, EPUB and Kindle. Book excerpt: What would it mean for American and African American literary studies if readers took the spirituality and travel of Black women seriously? With Spirit Deep: Recovering the Sacred in Black Women’s Travel, Tisha Brooks addresses this question by focusing on three nineteenth-century Black women writers who merged the spiritual and travel narrative genres: Zilpha Elaw, Amanda Smith, and Nancy Prince. Brooks hereby challenges the divides between religious and literary studies, and between coerced and "free" passages within travel writing studies to reveal meaningful new connections in Black women’s writings. Bringing together both sacred and secular texts, Spirit Deep uncovers an enduring spiritual legacy of movement and power that Black women have claimed for themselves in opposition to the single story of the Black (female) body as captive, monstrous, and strange. Spirit Deep thus addresses the marginalization of Black women from larger conversations about travel writing, demonstrating the continuing impact of their spirituality and movements in our present world.

Download or read book Deep Marine Systems written by Kevin T. Pickering and published by John Wiley & Sons. This book was released on 2015-10-23 with total page 672 pages. Available in PDF, EPUB and Kindle. Book excerpt: Deep-water (below wave base) processes, although generallyhidden from view, shape the sedimentary record of more than 65% ofthe Earth’s surface, including large parts of ancientmountain belts. This book aims to inform advanced-levelundergraduate and postgraduate students, and professional Earthscientists with interests in physical oceanography and hydrocarbonexploration and production, about many of the important physicalaspects of deep-water (mainly deep-marine) systems. The authorsconsider transport and deposition in the deep sea, trace-fossilassemblages, and facies stacking patterns as an archive of theunderlying controls on deposit architecture (e.g., seismicity,climate change, autocyclicity). Topics include modern and ancientdeep-water sedimentary environments, tectonic settings, and howbasinal and extra-basinal processes generate the typicalcharacteristics of basin slopes, submarine canyons, contouritemounds and drifts, submarine fans, basin floors and abyssalplains.

Download or read book Deep Value written by Tobias E. Carlisle and published by John Wiley & Sons. This book was released on 2014-08-18 with total page 245 pages. Available in PDF, EPUB and Kindle. Book excerpt: The economic climate is ripe for another golden age of shareholder activism Deep Value: Why Activist Investors and Other Contrarians Battle for Control of Losing Corporations is a must-read exploration of deep value investment strategy, describing the evolution of the theories of valuation and shareholder activism from Graham to Icahn and beyond. The book combines engaging anecdotes with industry research to illustrate the principles and methods of this complex strategy, and explains the reasoning behind seemingly incomprehensible activist maneuvers. Written by an active value investor, Deep Value provides an insider's perspective on shareholder activist strategies in a format accessible to both professional investors and laypeople. The Deep Value investment philosophy as described by Graham initially identified targets by their discount to liquidation value. This approach was extremely effective, but those opportunities are few and far between in the modern market, forcing activists to adapt. Current activists assess value from a much broader palate, and exploit a much wider range of tools to achieve their goals. Deep Value enumerates and expands upon the resources and strategies available to value investors today, and describes how the economic climate is allowing value investing to re-emerge. Topics include: Target identification, and determining the most advantageous ends Strategies and tactics of effective activism Unseating management and fomenting change Eyeing conditions for the next M&A boom Activist hedge funds have been quiet since the early 2000s, but economic conditions, shareholder sentiment, and available opportunities are creating a fertile environment for another golden age of activism. Deep Value: Why Activist Investors and Other Contrarians Battle for Control of Losing Corporations provides the in-depth information investors need to get up to speed before getting left behind.

Download or read book A Fire Upon The Deep written by Vernor Vinge and published by Tor Science Fiction. This book was released on 2010-04-01 with total page 626 pages. Available in PDF, EPUB and Kindle. Book excerpt: Now with a new introduction for the Tor Essentials line, A Fire Upon the Deep is sure to bring a new generation of SF fans to Vinge's award-winning works. A Hugo Award-winning Novel! “Vinge is one of the best visionary writers of SF today.”-David Brin Thousands of years in the future, humanity is no longer alone in a universe where a mind's potential is determined by its location in space, from superintelligent entities in the Transcend, to the limited minds of the Unthinking Depths, where only simple creatures, and technology, can function. Nobody knows what strange force partitioned space into these "regions of thought," but when the warring Straumli realm use an ancient Transcendent artifact as a weapon, they unwittingly unleash an awesome power that destroys thousands of worlds and enslaves all natural and artificial intelligence. Fleeing this galactic threat, Ravna crash lands on a strange world with a ship-hold full of cryogenically frozen children, the only survivors from a destroyed space-lab. They are taken captive by the Tines, an alien race with a harsh medieval culture, and used as pawns in a ruthless power struggle. Tor books by Vernor Vinge Zones of Thought Series A Fire Upon The Deep A Deepness In The Sky The Children of The Sky Realtime/Bobble Series The Peace War Marooned in Realtime Other Novels The Witling Tatja Grimm's World Rainbows End Collections Collected Stories of Vernor Vinge True Names At the Publisher's request, this title is being sold without Digital Rights Management Software (DRM) applied.

Download or read book Deep Reinforcement Learning written by Aske Plaat and published by Springer Nature. This book was released on 2022-06-10 with total page 414 pages. Available in PDF, EPUB and Kindle. Book excerpt: Deep reinforcement learning has attracted considerable attention recently. Impressive results have been achieved in such diverse fields as autonomous driving, game playing, molecular recombination, and robotics. In all these fields, computer programs have taught themselves to understand problems that were previously considered to be very difficult. In the game of Go, the program AlphaGo has even learned to outmatch three of the world’s leading players.Deep reinforcement learning takes its inspiration from the fields of biology and psychology. Biology has inspired the creation of artificial neural networks and deep learning, while psychology studies how animals and humans learn, and how subjects’ desired behavior can be reinforced with positive and negative stimuli. When we see how reinforcement learning teaches a simulated robot to walk, we are reminded of how children learn, through playful exploration. Techniques that are inspired by biology and psychology work amazingly well in computers: animal behavior and the structure of the brain as new blueprints for science and engineering. In fact, computers truly seem to possess aspects of human behavior; as such, this field goes to the heart of the dream of artificial intelligence. These research advances have not gone unnoticed by educators. Many universities have begun offering courses on the subject of deep reinforcement learning. The aim of this book is to provide an overview of the field, at the proper level of detail for a graduate course in artificial intelligence. It covers the complete field, from the basic algorithms of Deep Q-learning, to advanced topics such as multi-agent reinforcement learning and meta learning.

Download or read book Into the Deep written by V. E. Rosswell and published by Archipelago Pr. This book was released on 1998 with total page 300 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Federal Register written by and published by . This book was released on 1976 with total page 1314 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Network Intrusion Detection using Deep Learning written by Kwangjo Kim and published by Springer. This book was released on 2018-09-25 with total page 92 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents recent advances in intrusion detection systems (IDSs) using state-of-the-art deep learning methods. It also provides a systematic overview of classical machine learning and the latest developments in deep learning. In particular, it discusses deep learning applications in IDSs in different classes: generative, discriminative, and adversarial networks. Moreover, it compares various deep learning-based IDSs based on benchmarking datasets. The book also proposes two novel feature learning models: deep feature extraction and selection (D-FES) and fully unsupervised IDS. Further challenges and research directions are presented at the end of the book. Offering a comprehensive overview of deep learning-based IDS, the book is a valuable reerence resource for undergraduate and graduate students, as well as researchers and practitioners interested in deep learning and intrusion detection. Further, the comparison of various deep-learning applications helps readers gain a basic understanding of machine learning, and inspires applications in IDS and other related areas in cybersecurity.

Download or read book Deep Reinforcement Learning with Python written by Sudharsan Ravichandiran and published by Packt Publishing Ltd. This book was released on 2020-09-30 with total page 761 pages. Available in PDF, EPUB and Kindle. Book excerpt: An example-rich guide for beginners to start their reinforcement and deep reinforcement learning journey with state-of-the-art distinct algorithms Key FeaturesCovers a vast spectrum of basic-to-advanced RL algorithms with mathematical explanations of each algorithmLearn how to implement algorithms with code by following examples with line-by-line explanationsExplore the latest RL methodologies such as DDPG, PPO, and the use of expert demonstrationsBook Description With significant enhancements in the quality and quantity of algorithms in recent years, this second edition of Hands-On Reinforcement Learning with Python has been revamped into an example-rich guide to learning state-of-the-art reinforcement learning (RL) and deep RL algorithms with TensorFlow 2 and the OpenAI Gym toolkit. In addition to exploring RL basics and foundational concepts such as Bellman equation, Markov decision processes, and dynamic programming algorithms, this second edition dives deep into the full spectrum of value-based, policy-based, and actor-critic RL methods. It explores state-of-the-art algorithms such as DQN, TRPO, PPO and ACKTR, DDPG, TD3, and SAC in depth, demystifying the underlying math and demonstrating implementations through simple code examples. The book has several new chapters dedicated to new RL techniques, including distributional RL, imitation learning, inverse RL, and meta RL. You will learn to leverage stable baselines, an improvement of OpenAI’s baseline library, to effortlessly implement popular RL algorithms. The book concludes with an overview of promising approaches such as meta-learning and imagination augmented agents in research. By the end, you will become skilled in effectively employing RL and deep RL in your real-world projects. What you will learnUnderstand core RL concepts including the methodologies, math, and codeTrain an agent to solve Blackjack, FrozenLake, and many other problems using OpenAI GymTrain an agent to play Ms Pac-Man using a Deep Q NetworkLearn policy-based, value-based, and actor-critic methodsMaster the math behind DDPG, TD3, TRPO, PPO, and many othersExplore new avenues such as the distributional RL, meta RL, and inverse RLUse Stable Baselines to train an agent to walk and play Atari gamesWho this book is for If you’re a machine learning developer with little or no experience with neural networks interested in artificial intelligence and want to learn about reinforcement learning from scratch, this book is for you. Basic familiarity with linear algebra, calculus, and the Python programming language is required. Some experience with TensorFlow would be a plus.

Download or read book Practical Computer Vision Applications Using Deep Learning with CNNs written by Ahmed Fawzy Gad and published by Apress. This book was released on 2018-12-05 with total page 421 pages. Available in PDF, EPUB and Kindle. Book excerpt: Deploy deep learning applications into production across multiple platforms. You will work on computer vision applications that use the convolutional neural network (CNN) deep learning model and Python. This book starts by explaining the traditional machine-learning pipeline, where you will analyze an image dataset. Along the way you will cover artificial neural networks (ANNs), building one from scratch in Python, before optimizing it using genetic algorithms. For automating the process, the book highlights the limitations of traditional hand-crafted features for computer vision and why the CNN deep-learning model is the state-of-art solution. CNNs are discussed from scratch to demonstrate how they are different and more efficient than the fully connected ANN (FCNN). You will implement a CNN in Python to give you a full understanding of the model. After consolidating the basics, you will use TensorFlow to build a practical image-recognition model that you will deploy to a web server using Flask, making it accessible over the Internet. Using Kivy and NumPy, you will create cross-platform data science applications with low overheads. This book will help you apply deep learning and computer vision concepts from scratch, step-by-step from conception to production. What You Will Learn Understand how ANNs and CNNs work Create computer vision applications and CNNs from scratch using PythonFollow a deep learning project from conception to production using TensorFlowUse NumPy with Kivy to build cross-platform data science applications Who This Book Is ForData scientists, machine learning and deep learning engineers, software developers.

Download or read book Deep Generative Modeling written by Jakub M. Tomczak and published by Springer Nature. This book was released on 2022-02-18 with total page 210 pages. Available in PDF, EPUB and Kindle. Book excerpt: This textbook tackles the problem of formulating AI systems by combining probabilistic modeling and deep learning. Moreover, it goes beyond typical predictive modeling and brings together supervised learning and unsupervised learning. The resulting paradigm, called deep generative modeling, utilizes the generative perspective on perceiving the surrounding world. It assumes that each phenomenon is driven by an underlying generative process that defines a joint distribution over random variables and their stochastic interactions, i.e., how events occur and in what order. The adjective "deep" comes from the fact that the distribution is parameterized using deep neural networks. There are two distinct traits of deep generative modeling. First, the application of deep neural networks allows rich and flexible parameterization of distributions. Second, the principled manner of modeling stochastic dependencies using probability theory ensures rigorous formulation and prevents potential flaws in reasoning. Moreover, probability theory provides a unified framework where the likelihood function plays a crucial role in quantifying uncertainty and defining objective functions. Deep Generative Modeling is designed to appeal to curious students, engineers, and researchers with a modest mathematical background in undergraduate calculus, linear algebra, probability theory, and the basics in machine learning, deep learning, and programming in Python and PyTorch (or other deep learning libraries). It will appeal to students and researchers from a variety of backgrounds, including computer science, engineering, data science, physics, and bioinformatics, who wish to become familiar with deep generative modeling. To engage the reader, the book introduces fundamental concepts with specific examples and code snippets. The full code accompanying the book is available on github. The ultimate aim of the book is to outline the most important techniques in deep generative modeling and, eventually, enable readers to formulate new models and implement them.

Download or read book The Deep Learning Architect s Handbook written by Ee Kin Chin and published by Packt Publishing Ltd. This book was released on 2023-12-29 with total page 516 pages. Available in PDF, EPUB and Kindle. Book excerpt: Harness the power of deep learning to drive productivity and efficiency using this practical guide covering techniques and best practices for the entire deep learning life cycle Key Features Interpret your models’ decision-making process, ensuring transparency and trust in your AI-powered solutions Gain hands-on experience in every step of the deep learning life cycle Explore case studies and solutions for deploying DL models while addressing scalability, data drift, and ethical considerations Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionDeep learning enables previously unattainable feats in automation, but extracting real-world business value from it is a daunting task. This book will teach you how to build complex deep learning models and gain intuition for structuring your data to accomplish your deep learning objectives. This deep learning book explores every aspect of the deep learning life cycle, from planning and data preparation to model deployment and governance, using real-world scenarios that will take you through creating, deploying, and managing advanced solutions. You’ll also learn how to work with image, audio, text, and video data using deep learning architectures, as well as optimize and evaluate your deep learning models objectively to address issues such as bias, fairness, adversarial attacks, and model transparency. As you progress, you’ll harness the power of AI platforms to streamline the deep learning life cycle and leverage Python libraries and frameworks such as PyTorch, ONNX, Catalyst, MLFlow, Captum, Nvidia Triton, Prometheus, and Grafana to execute efficient deep learning architectures, optimize model performance, and streamline the deployment processes. You’ll also discover the transformative potential of large language models (LLMs) for a wide array of applications. By the end of this book, you'll have mastered deep learning techniques to unlock its full potential for your endeavors.What you will learn Use neural architecture search (NAS) to automate the design of artificial neural networks (ANNs) Implement recurrent neural networks (RNNs), convolutional neural networks (CNNs), BERT, transformers, and more to build your model Deal with multi-modal data drift in a production environment Evaluate the quality and bias of your models Explore techniques to protect your model from adversarial attacks Get to grips with deploying a model with DataRobot AutoML Who this book is for This book is for deep learning practitioners, data scientists, and machine learning developers who want to explore deep learning architectures to solve complex business problems. Professionals in the broader deep learning and AI space will also benefit from the insights provided, applicable across a variety of business use cases. Working knowledge of Python programming and a basic understanding of deep learning techniques is needed to get started with this book.