Perceptrons; an Introduction to Computational Geometry PDF Download
Are you looking for read ebook online? Search for your book and save it on your Kindle device, PC, phones or tablets. Download Perceptrons; an Introduction to Computational Geometry PDF full book. Access full book title Perceptrons; an Introduction to Computational Geometry by Marvin Minsky. Download full books in PDF and EPUB format.
Author: Marvin Minsky
Publisher:
ISBN:
Category : Computers
Languages : en
Pages : 272
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Book Description
Author: Marvin Minsky
Publisher:
ISBN:
Category : Computers
Languages : en
Pages : 272
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Author: Frank Rosenblatt
Publisher:
ISBN:
Category : Artificial intelligence
Languages : en
Pages : 290
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Author: Julien Mayor
Publisher: Frontiers E-books
ISBN: 2889192571
Category : Psychology
Languages : en
Pages : 181
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Book Description
This Research Topic aims to showcase the state of the art in language research while celebrating the 25th anniversary of the tremendously influential work of the PDP group, and the 50th anniversary of the perceptron. Although PDP models are often the gold standard to which new models are compared, the scope of this Research Topic is not constrained to connectionist models. Instead, we aimed to create a landmark forum in which experts in the field define the state of the art and future directions of the psychological processes underlying language learning and use, broadly defined. We thus called for papers involving computational modeling and original research as well as technical, philosophical, or historical discussions pertaining to models of cognition. We especially encouraged submissions aimed at contrasting different computational frameworks, and their relationship to imaging and behavioral data.
Author: Giuseppe Bonaccorso
Publisher: Packt Publishing Ltd
ISBN: 1788625900
Category : Computers
Languages : en
Pages : 567
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Book Description
Explore and master the most important algorithms for solving complex machine learning problems. Key Features Discover high-performing machine learning algorithms and understand how they work in depth. One-stop solution to mastering supervised, unsupervised, and semi-supervised machine learning algorithms and their implementation. Master concepts related to algorithm tuning, parameter optimization, and more Book Description Machine learning is a subset of AI that aims to make modern-day computer systems smarter and more intelligent. The real power of machine learning resides in its algorithms, which make even the most difficult things capable of being handled by machines. However, with the advancement in the technology and requirements of data, machines will have to be smarter than they are today to meet the overwhelming data needs; mastering these algorithms and using them optimally is the need of the hour. Mastering Machine Learning Algorithms is your complete guide to quickly getting to grips with popular machine learning algorithms. You will be introduced to the most widely used algorithms in supervised, unsupervised, and semi-supervised machine learning, and will learn how to use them in the best possible manner. Ranging from Bayesian models to the MCMC algorithm to Hidden Markov models, this book will teach you how to extract features from your dataset and perform dimensionality reduction by making use of Python-based libraries such as scikit-learn. You will also learn how to use Keras and TensorFlow to train effective neural networks. If you are looking for a single resource to study, implement, and solve end-to-end machine learning problems and use-cases, this is the book you need. What you will learn Explore how a ML model can be trained, optimized, and evaluated Understand how to create and learn static and dynamic probabilistic models Successfully cluster high-dimensional data and evaluate model accuracy Discover how artificial neural networks work and how to train, optimize, and validate them Work with Autoencoders and Generative Adversarial Networks Apply label spreading and propagation to large datasets Explore the most important Reinforcement Learning techniques Who this book is for This book is an ideal and relevant source of content for data science professionals who want to delve into complex machine learning algorithms, calibrate models, and improve the predictions of the trained model. A basic knowledge of machine learning is preferred to get the best out of this guide.
Author: Rajalingappaa Shanmugamani
Publisher: Packt Publishing Ltd
ISBN: 1788293355
Category : Computers
Languages : en
Pages : 304
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Book Description
Learn how to model and train advanced neural networks to implement a variety of Computer Vision tasks Key Features Train different kinds of deep learning model from scratch to solve specific problems in Computer Vision Combine the power of Python, Keras, and TensorFlow to build deep learning models for object detection, image classification, similarity learning, image captioning, and more Includes tips on optimizing and improving the performance of your models under various constraints Book Description Deep learning has shown its power in several application areas of Artificial Intelligence, especially in Computer Vision. Computer Vision is the science of understanding and manipulating images, and finds enormous applications in the areas of robotics, automation, and so on. This book will also show you, with practical examples, how to develop Computer Vision applications by leveraging the power of deep learning. In this book, you will learn different techniques related to object classification, object detection, image segmentation, captioning, image generation, face analysis, and more. You will also explore their applications using popular Python libraries such as TensorFlow and Keras. This book will help you master state-of-the-art, deep learning algorithms and their implementation. What you will learn Set up an environment for deep learning with Python, TensorFlow, and Keras Define and train a model for image and video classification Use features from a pre-trained Convolutional Neural Network model for image retrieval Understand and implement object detection using the real-world Pedestrian Detection scenario Learn about various problems in image captioning and how to overcome them by training images and text together Implement similarity matching and train a model for face recognition Understand the concept of generative models and use them for image generation Deploy your deep learning models and optimize them for high performance Who this book is for This book is targeted at data scientists and Computer Vision practitioners who wish to apply the concepts of Deep Learning to overcome any problem related to Computer Vision. A basic knowledge of programming in Python—and some understanding of machine learning concepts—is required to get the best out of this book.
Author: Fouad Sabry
Publisher: One Billion Knowledgeable
ISBN:
Category : Computers
Languages : en
Pages : 130
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Book Description
What Is Multilayer Perceptron A fully connected class of feedforward artificial neural network (ANN), a multilayer perceptron, or MLP, is referred to as a multilayer perceptron. The word "MLP" is used in a way that is rather vague. Sometimes it is used to refer to any feedforward ANN, and other times it is used more specifically to refer to networks that are constructed of several layers of perceptrons; for more information, see "Terminology." When they just contain one hidden layer, multilayer perceptrons are sometimes jokingly referred to as "vanilla" neural networks. This is especially true when the term is used in a slang context. How You Will Benefit (I) Insights, and validations about the following topics: Chapter 1: Multilayer Perceptron Chapter 2: Artificial Neural Network Chapter 3: Perceptron Chapter 4: Artificial Neuron Chapter 5: Activation Function Chapter 6: Backpropagation Chapter 7: Delta Rule Chapter 8: Feedforward Neural Network Chapter 9: Universal Approximation Theorem Chapter 10: Mathematics of Artificial Neural Networks (II) Answering the public top questions about multilayer perceptron. (III) Real world examples for the usage of multilayer perceptron in many fields. Who This Book Is For Professionals, undergraduate and graduate students, enthusiasts, hobbyists, and those who want to go beyond basic knowledge or information for any kind of multilayer perceptron. What Is Artificial Intelligence Series The Artificial Intelligence eBook series provides comprehensive coverage in over 200 topics. Each ebook covers a specific Artificial Intelligence topic in depth, written by experts in the field. The series aims to give readers a thorough understanding of the concepts, techniques, history and applications of artificial intelligence. Topics covered include machine learning, deep learning, neural networks, computer vision, natural language processing, robotics, ethics and more. The ebooks are written for professionals, students, and anyone interested in learning about the latest developments in this rapidly advancing field. The Artificial Intelligence eBook series provides an in-depth yet accessible exploration, from the fundamental concepts to the state-of-the-art research. With over 200 volumes, readers gain a thorough grounding in all aspects of Artificial Intelligence. The ebooks are designed to build knowledge systematically, with later volumes building on the foundations laid by earlier ones. This comprehensive series is an indispensable resource for anyone seeking to develop expertise in artificial intelligence.
Author: Frank Rosenblatt
Publisher:
ISBN:
Category : Brain
Languages : en
Pages : 94
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Author: Delip Rao
Publisher: O'Reilly Media
ISBN: 1491978201
Category : Computers
Languages : en
Pages : 256
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Book Description
Natural Language Processing (NLP) provides boundless opportunities for solving problems in artificial intelligence, making products such as Amazon Alexa and Google Translate possible. If you’re a developer or data scientist new to NLP and deep learning, this practical guide shows you how to apply these methods using PyTorch, a Python-based deep learning library. Authors Delip Rao and Brian McMahon provide you with a solid grounding in NLP and deep learning algorithms and demonstrate how to use PyTorch to build applications involving rich representations of text specific to the problems you face. Each chapter includes several code examples and illustrations. Explore computational graphs and the supervised learning paradigm Master the basics of the PyTorch optimized tensor manipulation library Get an overview of traditional NLP concepts and methods Learn the basic ideas involved in building neural networks Use embeddings to represent words, sentences, documents, and other features Explore sequence prediction and generate sequence-to-sequence models Learn design patterns for building production NLP systems
Author: Ke-Lin Du
Publisher: Springer Science & Business Media
ISBN: 1447155718
Category : Technology & Engineering
Languages : en
Pages : 834
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Book Description
Providing a broad but in-depth introduction to neural network and machine learning in a statistical framework, this book provides a single, comprehensive resource for study and further research. All the major popular neural network models and statistical learning approaches are covered with examples and exercises in every chapter to develop a practical working understanding of the content. Each of the twenty-five chapters includes state-of-the-art descriptions and important research results on the respective topics. The broad coverage includes the multilayer perceptron, the Hopfield network, associative memory models, clustering models and algorithms, the radial basis function network, recurrent neural networks, principal component analysis, nonnegative matrix factorization, independent component analysis, discriminant analysis, support vector machines, kernel methods, reinforcement learning, probabilistic and Bayesian networks, data fusion and ensemble learning, fuzzy sets and logic, neurofuzzy models, hardware implementations, and some machine learning topics. Applications to biometric/bioinformatics and data mining are also included. Focusing on the prominent accomplishments and their practical aspects, academic and technical staff, graduate students and researchers will find that this provides a solid foundation and encompassing reference for the fields of neural networks, pattern recognition, signal processing, machine learning, computational intelligence, and data mining.
Author: Joseph N. Wilson
Publisher: CRC Press
ISBN: 1420042386
Category : Computers
Languages : en
Pages : 444
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Book Description
Image algebra is a comprehensive, unifying theory of image transformations, image analysis, and image understanding. In 1996, the bestselling first edition of the Handbook of Computer Vision Algorithms in Image Algebra introduced engineers, scientists, and students to this powerful tool, its basic concepts, and its use in the concise representation
