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Author : Marvin Minsky
Publisher :
ISBN 13 :
Total Pages : 272 pages
Book Rating : 4.:/5 (318 download)
Download or read book Perceptrons; an Introduction to Computational Geometry written by Marvin Minsky and published by . This book was released on 1969 with total page 272 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author : Frank Rosenblatt
Publisher :
ISBN 13 :
Total Pages : 282 pages
Book Rating : 4.F/5 ( download)
Download or read book The Perceptron written by Frank Rosenblatt and published by . This book was released on 1958 with total page 282 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author : Julien Mayor
Publisher : Frontiers E-books
ISBN 13 : 2889192571
Total Pages : 181 pages
Book Rating : 4.8/5 (891 download)
Download or read book 50 years after the perceptron, 25 years after PDP: Neural computation in language sciences written by Julien Mayor and published by Frontiers E-books. This book was released on 2014-08-11 with total page 181 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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 : Daniel Shiffman
Publisher : No Starch Press
ISBN 13 : 1718503717
Total Pages : 0 pages
Book Rating : 4.7/5 (185 download)
Download or read book The Nature of Code written by Daniel Shiffman and published by No Starch Press. This book was released on 2024-09-03 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: All aboard The Coding Train! This beginner-friendly creative coding tutorial is designed to grow your skills in a fun, hands-on way as you build simulations of real-world phenomena with “The Coding Train” YouTube star Daniel Shiffman. How can we use code to capture the unpredictable properties of nature? How can understanding the mathematical principles behind our physical world help us create interesting digital environments? Written by “The Coding Train” YouTube star Daniel Shiffman, The Nature of Code is a beginner-friendly creative coding tutorial that explores a range of programming strategies for developing computer simulations of natural systems—from elementary concepts in math and physics to sophisticated machine-learning algorithms. Using the same enthusiastic style on display in Shiffman’s popular YT channel, this book makes learning to program fun, empowering you to generate fascinating graphical output while refining your problem-solving and algorithmic-thinking skills. You’ll progress from building a basic physics engine that simulates the effects of forces like gravity and wind resistance, to creating evolving systems of intelligent autonomous agents that can learn from their mistakes and adapt to their environment. The Nature of Code introduces important topics such as: Randomness Forces and vectors Trigonometry Cellular automata and fractals Genetic algorithms Neural networks Learn from an expert how to transform your beginner-level skills into writing well-organized, thoughtful programs that set the stage for further experiments in generative design. NOTE: All examples are written with p5.js, a JavaScript library for creative coding, and are available on the book's website.
Author : Giuseppe Bonaccorso
Publisher : Packt Publishing Ltd
ISBN 13 : 1788625900
Total Pages : 567 pages
Book Rating : 4.7/5 (886 download)
Download or read book Mastering Machine Learning Algorithms written by Giuseppe Bonaccorso and published by Packt Publishing Ltd. This book was released on 2018-05-25 with total page 567 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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 13 : 1788293355
Total Pages : 304 pages
Book Rating : 4.7/5 (882 download)
Download or read book Deep Learning for Computer Vision written by Rajalingappaa Shanmugamani and published by Packt Publishing Ltd. This book was released on 2018-01-23 with total page 304 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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 : Alessandro Parisi
Publisher : Packt Publishing Ltd
ISBN 13 : 1789805171
Total Pages : 331 pages
Book Rating : 4.7/5 (898 download)
Download or read book Hands-On Artificial Intelligence for Cybersecurity written by Alessandro Parisi and published by Packt Publishing Ltd. This book was released on 2019-08-02 with total page 331 pages. Available in PDF, EPUB and Kindle. Book excerpt: Build smart cybersecurity systems with the power of machine learning and deep learning to protect your corporate assets Key FeaturesIdentify and predict security threats using artificial intelligenceDevelop intelligent systems that can detect unusual and suspicious patterns and attacksLearn how to test the effectiveness of your AI cybersecurity algorithms and toolsBook Description Today's organizations spend billions of dollars globally on cybersecurity. Artificial intelligence has emerged as a great solution for building smarter and safer security systems that allow you to predict and detect suspicious network activity, such as phishing or unauthorized intrusions. This cybersecurity book presents and demonstrates popular and successful AI approaches and models that you can adapt to detect potential attacks and protect your corporate systems. You'll learn about the role of machine learning and neural networks, as well as deep learning in cybersecurity, and you'll also learn how you can infuse AI capabilities into building smart defensive mechanisms. As you advance, you'll be able to apply these strategies across a variety of applications, including spam filters, network intrusion detection, botnet detection, and secure authentication. By the end of this book, you'll be ready to develop intelligent systems that can detect unusual and suspicious patterns and attacks, thereby developing strong network security defenses using AI. What you will learnDetect email threats such as spamming and phishing using AICategorize APT, zero-days, and polymorphic malware samplesOvercome antivirus limits in threat detectionPredict network intrusions and detect anomalies with machine learningVerify the strength of biometric authentication procedures with deep learningEvaluate cybersecurity strategies and learn how you can improve themWho this book is for If you’re a cybersecurity professional or ethical hacker who wants to build intelligent systems using the power of machine learning and AI, you’ll find this book useful. Familiarity with cybersecurity concepts and knowledge of Python programming is essential to get the most out of this book.
Author : Fouad Sabry
Publisher : One Billion Knowledgeable
ISBN 13 :
Total Pages : 130 pages
Book Rating : 4.:/5 (661 download)
Download or read book Multilayer Perceptron written by Fouad Sabry and published by One Billion Knowledgeable. This book was released on 2023-06-26 with total page 130 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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 : Delip Rao
Publisher : O'Reilly Media
ISBN 13 : 1491978201
Total Pages : 256 pages
Book Rating : 4.4/5 (919 download)
Download or read book Natural Language Processing with PyTorch written by Delip Rao and published by O'Reilly Media. This book was released on 2019-01-22 with total page 256 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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 : Frank Rosenblatt
Publisher :
ISBN 13 :
Total Pages : 94 pages
Book Rating : 4.E/5 ( download)
Download or read book Two Theorems of Statistical Separability in the Perceptron written by Frank Rosenblatt and published by . This book was released on 1958 with total page 94 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author : Ke-Lin Du
Publisher : Springer Science & Business Media
ISBN 13 : 1447155718
Total Pages : 824 pages
Book Rating : 4.4/5 (471 download)
Download or read book Neural Networks and Statistical Learning written by Ke-Lin Du and published by Springer Science & Business Media. This book was released on 2013-12-09 with total page 824 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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 13 : 1420042386
Total Pages : 444 pages
Book Rating : 4.4/5 (2 download)
Download or read book Handbook of Computer Vision Algorithms in Image Algebra written by Joseph N. Wilson and published by CRC Press. This book was released on 2000-09-21 with total page 444 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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
Author : Giuseppe Ciaburro
Publisher : Packt Publishing Ltd
ISBN 13 : 1788399412
Total Pages : 270 pages
Book Rating : 4.7/5 (883 download)
Download or read book Neural Networks with R written by Giuseppe Ciaburro and published by Packt Publishing Ltd. This book was released on 2017-09-27 with total page 270 pages. Available in PDF, EPUB and Kindle. Book excerpt: Uncover the power of artificial neural networks by implementing them through R code. About This Book Develop a strong background in neural networks with R, to implement them in your applications Build smart systems using the power of deep learning Real-world case studies to illustrate the power of neural network models Who This Book Is For This book is intended for anyone who has a statistical background with knowledge in R and wants to work with neural networks to get better results from complex data. If you are interested in artificial intelligence and deep learning and you want to level up, then this book is what you need! What You Will Learn Set up R packages for neural networks and deep learning Understand the core concepts of artificial neural networks Understand neurons, perceptrons, bias, weights, and activation functions Implement supervised and unsupervised machine learning in R for neural networks Predict and classify data automatically using neural networks Evaluate and fine-tune the models you build. In Detail Neural networks are one of the most fascinating machine learning models for solving complex computational problems efficiently. Neural networks are used to solve wide range of problems in different areas of AI and machine learning. This book explains the niche aspects of neural networking and provides you with foundation to get started with advanced topics. The book begins with neural network design using the neural net package, then you'll build a solid foundation knowledge of how a neural network learns from data, and the principles behind it. This book covers various types of neural network including recurrent neural networks and convoluted neural networks. You will not only learn how to train neural networks, but will also explore generalization of these networks. Later we will delve into combining different neural network models and work with the real-world use cases. By the end of this book, you will learn to implement neural network models in your applications with the help of practical examples in the book. Style and approach A step-by-step guide filled with real-world practical examples.
Author : Josh Patterson
Publisher : "O'Reilly Media, Inc."
ISBN 13 : 1491914211
Total Pages : 532 pages
Book Rating : 4.4/5 (919 download)
Download or read book Deep Learning written by Josh Patterson and published by "O'Reilly Media, Inc.". This book was released on 2017-07-28 with total page 532 pages. Available in PDF, EPUB and Kindle. Book excerpt: Although interest in machine learning has reached a high point, lofty expectations often scuttle projects before they get very far. How can machine learning—especially deep neural networks—make a real difference in your organization? This hands-on guide not only provides the most practical information available on the subject, but also helps you get started building efficient deep learning networks. Authors Adam Gibson and Josh Patterson provide theory on deep learning before introducing their open-source Deeplearning4j (DL4J) library for developing production-class workflows. Through real-world examples, you’ll learn methods and strategies for training deep network architectures and running deep learning workflows on Spark and Hadoop with DL4J. Dive into machine learning concepts in general, as well as deep learning in particular Understand how deep networks evolved from neural network fundamentals Explore the major deep network architectures, including Convolutional and Recurrent Learn how to map specific deep networks to the right problem Walk through the fundamentals of tuning general neural networks and specific deep network architectures Use vectorization techniques for different data types with DataVec, DL4J’s workflow tool Learn how to use DL4J natively on Spark and Hadoop
Author : Ruth Vang-Mata
Publisher :
ISBN 13 : 9781536173642
Total Pages : 143 pages
Book Rating : 4.1/5 (736 download)
Download or read book Multilayer Perceptrons written by Ruth Vang-Mata and published by . This book was released on 2020 with total page 143 pages. Available in PDF, EPUB and Kindle. Book excerpt: "Multilayer Perceptrons: Theory and Applications opens with a review of research on the use of the multilayer perceptron artificial neural network method for solving ordinary/partial differential equations, accompanied by critical comments. A historical perspective on the evolution of the multilayer perceptron neural network is provided. Furthermore, the foundation for automated post-processing that is imperative for consolidating the signal data to a feature set is presented. In one study, panoramic dental x-ray images are used to estimate age and gender. These images were subjected to image pre-processing techniques to achieve better results. In a subsequent study, a multilayer perceptrons artificial neural network with one hidden layer and trained through the efficient resilient backpropagation algorithm is used for modeling quasi-fractal patch antennas. Later, the authors propose a scheme with eight steps for a dynamic time series forecasting using an adaptive multilayer perceptron with minimal complexity. Two different data sets from two different countries were used in the experiments to measure the robustness and accuracy of the models. In closing, a multilayer perceptron artificial neural network with a layer of hidden neurons is trained with the resilient backpropagation algorithm, and the network is used to model a Koch pre-fractal patch antenna"--
Author : Jeremy Watt
Publisher :
ISBN 13 : 1108575544
Total Pages : 598 pages
Book Rating : 4.1/5 (85 download)
Download or read book Machine Learning Refined written by Jeremy Watt and published by . This book was released on 2020-01-29 with total page 598 pages. Available in PDF, EPUB and Kindle. Book excerpt: An intuitive approach to machine learning covering key concepts, real-world applications, and practical Python coding exercises.
Author : Leonardo Vanneschi
Publisher : Springer Nature
ISBN 13 : 3031179226
Total Pages : 352 pages
Book Rating : 4.0/5 (311 download)
Download or read book Lectures on Intelligent Systems written by Leonardo Vanneschi and published by Springer Nature. This book was released on 2023-01-13 with total page 352 pages. Available in PDF, EPUB and Kindle. Book excerpt: This textbook provides the reader with an essential understanding of computational methods for intelligent systems. These are defined as systems that can solve problems autonomously, in particular problems where algorithmic solutions are inconceivable for humans or not practically executable by computers. Despite the rapidly growing applications in this field, the book avoids application details, instead focusing on computational methods that equip the reader with the methodological tools and competencies necessary to tackle current and future complex applications. The book consists of two parts: computational intelligence methods for optimization, and machine learning. Part I begins with the concept of optimization, and introduces local search algorithms, genetic algorithms, and particle swarm optimization. Part II begins with an introduction to machine learning and covers several methods, many of which can be used as supervised learning algorithms, such as decision tree learning, artificial neural networks, genetic programming, Bayesian learning, support vector machines, and ensemble methods, plus a discussion of unsupervised learning. This textbook is written in a self-contained style, suitable for undergraduate or graduate students in computer science and engineering, and for self-study by researchers and practitioners.
