Arama Sonuçları - Don Ihde~

Don Kişot / Yazar: Cervantes, Miguel De

The history of don quixote / Yazar: Saavedra, Miguel De Cervantes

Don Rigoberto'nun not defterleri : roman / Yazar: Llosa, Mario Vargas

“…los cuadernos de don Rigoberto…”

Oyuncular / Yazar: DeLillo, Don

Principles of Spread-Spectrum Communication Systems Yazar: Torrieri, Don

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Yaşanmış şiir : Don Kişot / Yazar: Garaudy,Roger

Computational Botany Methods for Automated Species Identification / Yazar: Remagnino, Paolo, Mayo, Simon, Wilkin, Paul, Cope, James, Kirkup, Don

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Güç : nasıl kazanır, nasıl elde tutarsınız / Yazar: Pfeffer, Jeffrey

Diversity and triumphs of navigating the terrain of academe : international perspectives /

İçindekiler: “…Prelims -- The great gulf between our progress yesterday and our survival tomorrow: swimming in the academe ocean -- Talk is cheap: the prospects and problems with campus conversations on race -- Navigating through PHD programs: experiences of Ghanaian PHD graduates from universities across the globe -- Walk steady, keep going: navigational moves of empowerment, resistance, and sustenance in the academy -- Progressing culturally responsive assessment for higher education institutions -- Navigating the politics of the academe -- Negotiating university teaching in Canada using critical race theory: having to continually prove oneself in academia -- "She don't [does not] belong here:" silver linings amid the ivory tower -- Index.…”
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Perspectives on diverse student identities in higher education : international perspectives on equity and inclusion /

İçindekiler: “…-- About the authors Name index -- Subject index.…”
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Practical Information Security A Competency-Based Education Course / Yazar: Alsmadi, Izzat, Burdwell, Robert, Aleroud , Ahmed, Wahbeh, Abdallah, Al-Qudah, Mahmoud, Al-Omari, Ahmad

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Designing Machine Learning Systems. Yazar: Huyen, Chip

İçindekiler: “…Infrastructure and Tooling for MLOps -- Storage and Compute -- Public Cloud Versus Private Data Centers -- Development Environment -- Dev Environment Setup -- Standardizing Dev Environments -- From Dev to Prod: Containers -- Resource Management -- Cron, Schedulers, and Orchestrators -- Data Science Workflow Management -- ML Platform -- Model Deployment -- Model Store -- Feature Store -- Build Versus Buy -- Summary -- Chapter 11. The Human Side of Machine Learning -- User Experience -- Ensuring User Experience Consistency -- Combatting "Mostly Correct" Predictions -- Smooth Failing -- Team Structure -- Cross-functional Teams Collaboration -- End-to-End Data Scientists -- Responsible AI -- Irresponsible AI: Case Studies -- A Framework for Responsible AI -- Summary -- Epilogue -- Index -- About the Author -- Colophon.…”
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Future Studies and Counterfactual Analysis Seeds of the Future / Yazar: Gordon, Theodore J., Todorova, Mariana

İçindekiler: “…Civility is Almost Gone, Debate is Almost Non-existent, yet the Sides are Firm in Their Positions and Sure They are Right -- Chapter 13: The Perfect Human: Unlocking the Mysteries of Human Genetics and Developing the Technologies to Manipulate Heredity Will Give, for Better or Worse, a New Dimension to Our Ability toSet the Fate of Our Children -- Chapter 14: Conclusions -- Appendix A Methodologies -- Appendix B Authors' Biographies -- Index.…”
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Environmental contexts and disability

İçindekiler: “…[et al.] -- "I can reinvent myself out here" : experiences of nature inclusion and mental wellbeing / Sonya L. Jakubec, Don Carruthers Den Hoed, Heather Ray -- Encounters with inaccessibility : the contexts women with spinal cord injury face when seeking gynecological health care / Catherine Lysack, Heather Dillaway -- An examination of systems-level barriers to employment for persons with vision loss / Chelsea Mohler ... …”
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A NIME Reader Fifteen Years of New Interfaces for Musical Expression /

İçindekiler: “…2001: Principles for Designing Computer Music Controllers -- 2001: Problems and Prospects for Intimate Musical Control of Computers -- 2002: The importance of parameter mapping in electronic instrument design -- 2002: Multimodal Interaction in Music Using the Electromyogram and Relative Position Sensing -- 2002: The Plank: Designing a Simple Haptic Controller -- 2003: Contexts of Collaborative Musical Experiences -- 2003: Sonigraphical Instruments: From FMOL to the reacTable* -- 2003: Designing, Playing, and Performing with a Vision-based Mouth Interface -- 2003: OpenSound Control: State of the Art 2003 -- 2004: The Electronic Sitar Controller -- 2004: PebbleBox and CrumbleBag: Tactile Interfaces for Granular Synthesis -- 2004: Toward a Generalized Friction Controller: From the Bowed String to Unusual Musical Instruments -- 2004: On-the-fly Programming: Using Code as an Expressive Musical Instrument -- 2005: Towards a Dimension Space for Musical Devices -- 2005: The Overtone Violin -- 2006: Sensemble: A Wireless, Compact, Multi-User Sensor System for Interactive Dance -- 2006: Mobile Music Technology: Report on an Emerging Community -- 2007: Wireless Sensor Interface and Gesture-Follower for Music Pedagogy -- 2007: Don't Forget the Laptop: Using Native Input Capabilities for Expressive Musical Control -- 2007: Expression and its Discontents: Toward an Ecology of Musical Creation -- 2007: The Acoustic, the Digital and the Body: A Survey on Musical Instruments -- 2008: Eight Years of Practice on the Hyper-Flute: Technological and Musical Perspectives -- 2009: A History of Hemispherical Speakers at Princeton, Plus a DIY Guide -- 2011: Satellite CCRMA: A Musical Interaction and Sound Synthesis Platform -- 2012: The Fingerphone: a Case Study of Sustainable Instrument Redesign -- 2012: To be inside someone else's dream: Music for Sleeping & Waking Minds -- 2012:TouchKeys: Capacitive Multi-Touch Sensing on a Physical Keyboard -- 2013: The Web Browser As Synthesizer And Interface -- 2014: To Gesture or Not? …”
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Early education and care, and reconceptualizing play

İçindekiler: “…Manning, Anne Daniel -- "Air is a kind of wind": argumentation and the construction of knowledge / Sue Dockett, Bob Perry -- "We don't play that way at preschool": the moral and ethical dimensions of controlling children's play / Mac H. …”
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Deep Learning with TensorFlow and Keras - 3rd Edition : Build and Deploy Supervised, Unsupervised, Deep, and Reinforcement Learning Models. Yazar: Kapoor, Amita

İçindekiler: “…Multiple and multivariate linear regression using the TensorFlow Keras API -- Classification tasks and decision boundaries -- Logistic regression -- Logistic regression on the MNIST dataset -- Summary -- References -- Chapter 3: Convolutional Neural Networks -- Deep convolutional neural networks -- Local receptive fields -- Shared weights and bias -- A mathematical example -- ConvNets in TensorFlow -- Pooling layers -- Max pooling -- Average pooling -- ConvNets summary -- An example of DCNN: LeNet -- LeNet code in TF -- Understanding the power of deep learning -- Recognizing CIFAR-10 images with deep learning -- Improving the CIFAR-10 performance with a deeper network -- Improving the CIFAR-10 performance with data augmentation -- Predicting with CIFAR-10 -- Very deep convolutional networks for large-scale image recognition -- Recognizing cats with a VGG16 net work -- Utilizing the tf.Keras built-in VGG16 net module -- Recycling pre-built deep learning models for extracting features -- Deep Inception V3 for transfer learning -- Other CNN architectures -- AlexNet -- Residual networks -- HighwayNets and DenseNets -- Xception -- Style transfer -- Content distance -- Style distance -- Summary -- References -- Chapter 4: Word Embeddings -- Word embedding ‒ origins and fundamentals -- Distributed representations -- Static embeddings -- Word2Vec -- GloVe -- Creating your own embeddings using Gensim -- Exploring the embedding space with Gensim -- Using word embeddings for spam detection -- Getting the data -- Making the data ready for use -- Building the embedding matrix -- Defining the spam classifier -- Training and evaluating the model -- Running the spam detector -- Neural embeddings - not just for words -- Item2Vec -- node2vec -- Character and subword embeddings -- Dynamic embeddings -- Sentence and paragraph embeddings.…”
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Seismic Evaluation and Design of Petrochemical and Other Industrial Facilities. Yazar: Facilities, Task Committee on Seismic Evaluation and Design of Petrochemical

İçindekiler: “…Reference -- Chapter 9: Post-Earthquake Damage Assessment -- 9.1 Introduction -- 9.1.1 Assessment Priorities -- 9.1.2 Assessment Triggers -- 9.2 Pre-Investigation Activities -- 9.3 Performing Field Inspections -- 9.4 Evaluation of Load-Carrying Systems -- 9.5 Identification Of Damaged Structures -- 9.6 Documentation -- 9.7 Inspection Team -- 9.8 Recommended Equipment -- References -- Chapter 10: Retrofit Design -- 10.1 Introduction -- 10.2 Upgrade Situations -- 10.3 Criteria for Voluntary Seismic Upgrading -- 10.4 Seismic Retrofit Considerations for Plant Structures and Buildings -- 10.4.1 Review of Seismic Retrofit Scheme -- 10.4.2 Seismic Retrofit Objectives -- 10.4.3 Retrofit Design Requirements and Methods -- References -- Chapter 11: New and Existing Marine oil Terminals -- 11.1 Introduction -- 11.2 Mot Descriptions -- 11.3 Historical Performance -- 11.4 State of Practice -- 11.5 Overall Approach -- 11.5.1 OLE Performance Criteria -- 11.5.2 CLE Performance Criteria -- 11.5.3 Limit State Criteria -- 11.5.4 Seismic Analysis -- 11.6 Existing Marine Oil Terminals -- References -- Chapter 12: International Codes -- 12.1 Introduction -- 12.2 Code Conformance -- 12.3 Multiple Code Conformance -- 12.4 Cautions When Performing Code Comparisons -- 12.4.1 Don't Be Fooled by the Return Period -- 12.4.2 Compare Both Capacity and Demand -- 12.4.3 Know What ``Important'' Means -- 12.4.4 Be Aware of Why -- 12.4.5 It's Not Just the Capacity and Demand That Matter -- 12.4.6 Understand the Overall Philosophy -- 12.5 Vendor Issues -- 12.6 Language Issues -- References -- Nomenclature -- Index.…”
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Deep Learning with Python, Second Edition. Yazar: Chollet, Francois

İçindekiler: “…9.3.5 Putting it together: A mini Xception-like model -- 9.4 Interpreting what convnets learn -- 9.4.1 Visualizing intermediate activations -- 9.4.2 Visualizing convnet filters -- 9.4.3 Visualizing heatmaps of class activation -- Summary -- 10 Deep learning for timeseries -- 10.1 Different kinds of timeseries tasks -- 10.2 A temperature-forecasting example -- 10.2.1 Preparing the data -- 10.2.2 A common-sense, non-machine learning baseline -- 10.2.3 Let's try a basic machine learning model -- 10.2.4 Let's try a 1D convolutional model -- 10.2.5 A first recurrent baseline -- 10.3 Understanding recurrent neural networks -- 10.3.1 A recurrent layer in Keras -- 10.4 Advanced use of recurrent neural networks -- 10.4.1 Using recurrent dropout to fight overfitting -- 10.4.2 Stacking recurrent layers -- 10.4.3 Using bidirectional RNNs -- 10.4.4 Going even further -- Summary -- 11 Deep learning for text -- 11.1 Natural language processing: The bird's eye view -- 11.2 Preparing text data -- 11.2.1 Text standardization -- 11.2.2 Text splitting (tokenization) -- 11.2.3 Vocabulary indexing -- 11.2.4 Using the TextVectorization layer -- 11.3 Two approaches for representing groups of words: Sets and sequences -- 11.3.1 Preparing the IMDB movie reviews data -- 11.3.2 Processing words as a set: The bag-of-words approach -- 11.3.3 Processing words as a sequence: The sequence model approach -- 11.4 The Transformer architecture -- 11.4.1 Understanding self-attention -- 11.4.2 Multi-head attention -- 11.4.3 The Transformer encoder -- 11.4.4 When to use sequence models over bag-of-words models -- 11.5 Beyond text classification: Sequence-to-sequence learning -- 11.5.1 A machine translation example -- 11.5.2 Sequence-to-sequence learning with RNNs -- 11.5.3 Sequence-to-sequence learning with Transformer -- Summary -- 12 Generative deep learning -- 12.1 Text generation.…”
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