Download or read book Classified List of Publications of the Carnegie Institution of Washington written by Carnegie Institution of Washington and published by . This book was released on 1918 with total page 172 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book National Standard Reference Data System Publication List 1964 1979 written by and published by . This book was released on 1980 with total page 60 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book The Publishers Trade List Annual written by and published by . This book was released on 1973 with total page 1812 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book List of Available Publications Pacific Northwest Forest and Range Experiment Station written by Pacific Northwest Forest and Range Experiment Station (Portland, Or.) and published by . This book was released on 1965 with total page 76 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Title List of Documents Made Publicly Available written by and published by . This book was released on 1980 with total page 944 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Practical Deep Learning written by Ronald T. Kneusel and published by No Starch Press. This book was released on 2021-03-16 with total page 463 pages. Available in PDF, EPUB and Kindle. Book excerpt: Practical Deep Learning teaches total beginners how to build the datasets and models needed to train neural networks for your own DL projects. If you’ve been curious about machine learning but didn’t know where to start, this is the book you’ve been waiting for. Focusing on the subfield of machine learning known as deep learning, it explains core concepts and gives you the foundation you need to start building your own models. Rather than simply outlining recipes for using existing toolkits, Practical Deep Learning teaches you the why of deep learning and will inspire you to explore further. All you need is basic familiarity with computer programming and high school math—the book will cover the rest. After an introduction to Python, you’ll move through key topics like how to build a good training dataset, work with the scikit-learn and Keras libraries, and evaluate your models’ performance. You’ll also learn: How to use classic machine learning models like k-Nearest Neighbors, Random Forests, and Support Vector Machines How neural networks work and how they’re trained How to use convolutional neural networks How to develop a successful deep learning model from scratch You’ll conduct experiments along the way, building to a final case study that incorporates everything you’ve learned. The perfect introduction to this dynamic, ever-expanding field, Practical Deep Learning will give you the skills and confidence to dive into your own machine learning projects.

Download or read book P NP and NP Completeness written by Oded Goldreich and published by Cambridge University Press. This book was released on 2010-08-16 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: The focus of this book is the P versus NP Question and the theory of NP-completeness. It also provides adequate preliminaries regarding computational problems and computational models. The P versus NP Question asks whether or not finding solutions is harder than checking the correctness of solutions. An alternative formulation asks whether or not discovering proofs is harder than verifying their correctness. It is widely believed that the answer to these equivalent formulations is positive, and this is captured by saying that P is different from NP. Although the P versus NP Question remains unresolved, the theory of NP-completeness offers evidence for the intractability of specific problems in NP by showing that they are universal for the entire class. Amazingly enough, NP-complete problems exist, and furthermore hundreds of natural computational problems arising in many different areas of mathematics and science are NP-complete.

Download or read book N n and N d Interactions a Compilation written by Lawrence Berkeley Laboratory. Particle Data Group and published by . This book was released on 1972 with total page 290 pages. Available in PDF, EPUB and Kindle. Book excerpt: We compile 181 papers report p̄p, p̄n, p̄d, n̄p, n̄n, and n̄d reactions in flight for beam momenta up to 50 GeV/c. We display, as a function of incident momentum, total and channel cross sections, elastic and two-body inelastic differential cross sections, and polarization in elastic scattering, as well as our fits to the elastic differential cross sections. We also provide indices to the papers, as well as a complete listing of the selected data. The approximate cutoff date for the literature search for this report was September 1, 1971.

Download or read book Python Data Science Handbook written by Jake VanderPlas and published by "O'Reilly Media, Inc.". This book was released on 2016-11-21 with total page 548 pages. Available in PDF, EPUB and Kindle. Book excerpt: For many researchers, Python is a first-class tool mainly because of its libraries for storing, manipulating, and gaining insight from data. Several resources exist for individual pieces of this data science stack, but only with the Python Data Science Handbook do you get them all—IPython, NumPy, Pandas, Matplotlib, Scikit-Learn, and other related tools. Working scientists and data crunchers familiar with reading and writing Python code will find this comprehensive desk reference ideal for tackling day-to-day issues: manipulating, transforming, and cleaning data; visualizing different types of data; and using data to build statistical or machine learning models. Quite simply, this is the must-have reference for scientific computing in Python. With this handbook, you’ll learn how to use: IPython and Jupyter: provide computational environments for data scientists using Python NumPy: includes the ndarray for efficient storage and manipulation of dense data arrays in Python Pandas: features the DataFrame for efficient storage and manipulation of labeled/columnar data in Python Matplotlib: includes capabilities for a flexible range of data visualizations in Python Scikit-Learn: for efficient and clean Python implementations of the most important and established machine learning algorithms

Download or read book Data Analysis and Visualization Using Python written by Dr. Ossama Embarak and published by Apress. This book was released on 2018-11-20 with total page 390 pages. Available in PDF, EPUB and Kindle. Book excerpt: Look at Python from a data science point of view and learn proven techniques for data visualization as used in making critical business decisions. Starting with an introduction to data science with Python, you will take a closer look at the Python environment and get acquainted with editors such as Jupyter Notebook and Spyder. After going through a primer on Python programming, you will grasp fundamental Python programming techniques used in data science. Moving on to data visualization, you will see how it caters to modern business needs and forms a key factor in decision-making. You will also take a look at some popular data visualization libraries in Python. Shifting focus to data structures, you will learn the various aspects of data structures from a data science perspective. You will then work with file I/O and regular expressions in Python, followed by gathering and cleaning data. Moving on to exploring and analyzing data, you will look at advanced data structures in Python. Then, you will take a deep dive into data visualization techniques, going through a number of plotting systems in Python. In conclusion, you will complete a detailed case study, where you’ll get a chance to revisit the concepts you’ve covered so far. What You Will LearnUse Python programming techniques for data science Master data collections in Python Create engaging visualizations for BI systems Deploy effective strategies for gathering and cleaning data Integrate the Seaborn and Matplotlib plotting systems Who This Book Is For Developers with basic Python programming knowledge looking to adopt key strategies for data analysis and visualizations using Python.

Download or read book Python 3 Data Visualization Using Google Gemini written by Oswald Campesato and published by Walter de Gruyter GmbH & Co KG. This book was released on 2024-03-13 with total page 200 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book offers a comprehensive guide to leveraging Python-based data visualization techniques with the innovative capabilities of Google Gemini. Tailored for individuals proficient in Python seeking to enhancetheir visualization skills, it explores essential libraries like Pandas, Matplotlib, and Seaborn, along with insights into the innovative Gemini platform. With a focus on practicality and efficiency, it delivers a rapid yet thorough exploration of data visualization methodologies, supported by Gemini-generated code samples. Companion files with source code and figures are available for downloading. FEATURES: Covers Python-based data visualization libraries and techniques Includes practical examples and Gemini-generated code samples for efficient learning Integrates Google Gemini for advanced data visualization capabilities Sets up a conducive development environment for a seamless coding experience Includes companion files for downloading with source code and figures

Download or read book Energy Research Abstracts written by and published by . This book was released on 1989 with total page 660 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Advanced Deep Learning with TensorFlow 2 and Keras written by Rowel Atienza and published by Packt Publishing Ltd. This book was released on 2020-02-28 with total page 513 pages. Available in PDF, EPUB and Kindle. Book excerpt: Updated and revised second edition of the bestselling guide to advanced deep learning with TensorFlow 2 and Keras Key FeaturesExplore the most advanced deep learning techniques that drive modern AI resultsNew coverage of unsupervised deep learning using mutual information, object detection, and semantic segmentationCompletely updated for TensorFlow 2.xBook Description Advanced Deep Learning with TensorFlow 2 and Keras, Second Edition is a completely updated edition of the bestselling guide to the advanced deep learning techniques available today. Revised for TensorFlow 2.x, this edition introduces you to the practical side of deep learning with new chapters on unsupervised learning using mutual information, object detection (SSD), and semantic segmentation (FCN and PSPNet), further allowing you to create your own cutting-edge AI projects. Using Keras as an open-source deep learning library, the book features hands-on projects that show you how to create more effective AI with the most up-to-date techniques. Starting with an overview of multi-layer perceptrons (MLPs), convolutional neural networks (CNNs), and recurrent neural networks (RNNs), the book then introduces more cutting-edge techniques as you explore deep neural network architectures, including ResNet and DenseNet, and how to create autoencoders. You will then learn about GANs, and how they can unlock new levels of AI performance. Next, you’ll discover how a variational autoencoder (VAE) is implemented, and how GANs and VAEs have the generative power to synthesize data that can be extremely convincing to humans. You'll also learn to implement DRL such as Deep Q-Learning and Policy Gradient Methods, which are critical to many modern results in AI. What you will learnUse mutual information maximization techniques to perform unsupervised learningUse segmentation to identify the pixel-wise class of each object in an imageIdentify both the bounding box and class of objects in an image using object detectionLearn the building blocks for advanced techniques - MLPss, CNN, and RNNsUnderstand deep neural networks - including ResNet and DenseNetUnderstand and build autoregressive models – autoencoders, VAEs, and GANsDiscover and implement deep reinforcement learning methodsWho this book is for This is not an introductory book, so fluency with Python is required. The reader should also be familiar with some machine learning approaches, and practical experience with DL will also be helpful. Knowledge of Keras or TensorFlow 2.0 is not required but is recommended.

Download or read book Acid Mine Drainage Rock Drainage and Acid Sulfate Soils written by James A. Jacobs and published by John Wiley & Sons. This book was released on 2014-04-28 with total page 502 pages. Available in PDF, EPUB and Kindle. Book excerpt: Provides the tools needed to analyze and solve acid drainage problems Featuring contributions from leading experts in science and engineering, this book explores the complex biogeochemistry of acid mine drainage, rock drainage, and acid sulfate soils. It describes how to predict, prevent, and remediate the environmental impact of acid drainage and the oxidation of sulfides, offering the latest sampling and analytical methods. Moreover, readers will discover new approaches for recovering valuable resources from acid mine drainage, including bioleaching. Acid Mine Drainage, Rock Drainage, and Acid Sulfate Soils reviews the most current findings in the field, offering new insights into the underlying causes as well as new tools to minimize the harm of acid drainage: Part I: Causes of Acid Mine Drainage, Rock Drainage and Sulfate Soils focuses on the biogeochemistry of acid drainage in different environments. Part II: Assessment of Acid Mine Drainage, Rock Drainage and Sulfate Soils covers stream characterization, aquatic and biological sampling, evaluation of aquatic resources, and some unusual aspects of sulfide oxidation. Part III: Prediction and Prevention of Acid Drainage discusses acid-base accounting, kinetic testing, block modeling, petrology, and mineralogy studies. It also explains relevant policy and regulations. Part IV: Remediation of Acid Drainage, Rock Drainage and Sulfate Soils examines both passive and active cleanup methods to remediate acid drainage. Case studies from a variety of geologic settings highlight various approaches to analyzing and solving acid drainage problems. Replete with helpful appendices and an extensive list of web resources, Acid Mine Drainage, Rock Drainage, and Acid Sulfate Soils is recommended for mining engineers and scientists, regulatory officials, environmental scientists, land developers, and students.

Download or read book A Tour of Data Science written by Nailong Zhang and published by CRC Press. This book was released on 2020-11-11 with total page 217 pages. Available in PDF, EPUB and Kindle. Book excerpt: A Tour of Data Science: Learn R and Python in Parallel covers the fundamentals of data science, including programming, statistics, optimization, and machine learning in a single short book. It does not cover everything, but rather, teaches the key concepts and topics in Data Science. It also covers two of the most popular programming languages used in Data Science, R and Python, in one source. Key features: Allows you to learn R and Python in parallel Cover statistics, programming, optimization and predictive modelling, and the popular data manipulation tools – data.table and pandas Provides a concise and accessible presentation Includes machine learning algorithms implemented from scratch, linear regression, lasso, ridge, logistic regression, gradient boosting trees, etc. Appealing to data scientists, statisticians, quantitative analysts, and others who want to learn programming with R and Python from a data science perspective.

Download or read book Word Grammar written by Kensei Sugayama and published by A&C Black. This book was released on 2005-12-15 with total page 252 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is an introduction to Word Grammar, a theory of language structure founded and developed by Dick Hudson. In this theory, language is a cognitive network - a network of concepts, words and meanings containing all the elements of a linguistic analysis. The theory of language is therefore embedded in a theory of knowledge, in which there are no boundaries between one form of knowledge and any other. The most controversial idea in Word Grammar syntax is that phrase structure is redundant, because all its work can be done by means of dependencies between individual words. Word-word dependency is therefore a key concept in Word Grammar, and the syntax and semantics of a sentence is built upon this foundation. Contributors to this volume are primarily Word Grammar grammarians from across the world. All the chapters here manifest theoretical potentialities of Word Grammar, exploring how powerful Word Grammar is to offer analysis for linguistic phenomena in various languages. The chapters come from varying perspectives and include work on a number of languages, including English, German, Japanese, Swahili, Turkish and Ancient Greek. Phenomena studied include verbal inflection, case agreement, extraction, construction and code-mixing. This collection will be of interest to academics encountering Word Grammar for the first time, or for those who are already familiar with this theory and are interested in reading how it has evolved and what its future may hold.

Download or read book Evolutionary Deep Learning written by Michael Lanham and published by Simon and Schuster. This book was released on 2023-07-18 with total page 358 pages. Available in PDF, EPUB and Kindle. Book excerpt: Discover one-of-a-kind AI strategies never before seen outside of academic papers! Learn how the principles of evolutionary computation overcome deep learning’s common pitfalls and deliver adaptable model upgrades without constant manual adjustment. Evolutionary Deep Learning is a guide to improving your deep learning models with AutoML enhancements based on the principles of biological evolution. This exciting new approach utilizes lesser- known AI approaches to boost performance without hours of data annotation or model hyperparameter tuning. Google Colab notebooks make it easy to experiment and play around with each exciting example. By the time you’ve finished reading Evolutionary Deep Learning, you’ll be ready to build deep learning models as self-sufficient systems you can efficiently adapt to changing requirements. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.