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EBookClubs

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Book Advanced Algorithms for Neural Networks

Download or read book Advanced Algorithms for Neural Networks written by Timothy Masters and published by . This book was released on 1995-04-17 with total page 456 pages. Available in PDF, EPUB and Kindle. Book excerpt: This is one of the first books to offer practical in-depth coverage of the Probabilistic Neural Network (PNN) and several other neural nets and their related algorithms critical to solving some of today's toughest real-world computing problems. Includes complete C++ source code for basic and advanced applications.

Book Neural Networks in Finance

Download or read book Neural Networks in Finance written by Paul D. McNelis and published by Academic Press. This book was released on 2005-01-05 with total page 262 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book explores the intuitive appeal of neural networks and the genetic algorithm in finance. It demonstrates how neural networks used in combination with evolutionary computation outperform classical econometric methods for accuracy in forecasting, classification and dimensionality reduction. McNelis utilizes a variety of examples, from forecasting automobile production and corporate bond spread, to inflation and deflation processes in Hong Kong and Japan, to credit card default in Germany to bank failures in Texas, to cap-floor volatilities in New York and Hong Kong. * Offers a balanced, critical review of the neural network methods and genetic algorithms used in finance * Includes numerous examples and applications * Numerical illustrations use MATLAB code and the book is accompanied by a website

Book Machine Learning

    Book Details:
  • Author : Victor Sage
  • Publisher : Publishdrive
  • Release : 2023-12-05
  • ISBN : 9789635225514
  • Pages : 0 pages

Download or read book Machine Learning written by Victor Sage and published by Publishdrive. This book was released on 2023-12-05 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Few subjects have attracted as much interest, curiosity, and transformational power as machine learning (ML) in the enormous field of technological evolution. Machine learning has a huge and subtle impact on how we engage with technology, from the voice-activated assistants on our smartphones to the recommendation engines on our preferred streaming services. However, numerous individuals are still confused about the fundamental workings of machine learning, even in spite of its widespread use. This e-book aims to solve that puzzle. More than just an e-book for understanding the math and algorithms behind this potent technology, "Machine Learning: Unlocking Patterns and Insights with Advanced Algorithms" goes beyond that. It's a primer on how patterns arise from chaos and how machines learn from these patterns to anticipate the future. It's an investigation of the very fabric of our data-driven world. This e-book is suitable for readers of all levels, from the beginning data scientist who is keen to delve into the nuances of neural networks to the interested enthusiast who just wants to grasp the fundamentals. Before diving further into the fundamental algorithms that drive machine learning, we'll travel through time to trace the field's beginnings. Along the way, we'll address the difficulties this technology presents, such as ethical dilemmas and bias concerns, and investigate the plethora of practical uses that have transformed entire sectors.

Book Neural Networks and Deep Learning

Download or read book Neural Networks and Deep Learning written by Charu C. Aggarwal and published by Springer. This book was released on 2018-08-25 with total page 512 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book covers both classical and modern models in deep learning. The primary focus is on the theory and algorithms of deep learning. The theory and algorithms of neural networks are particularly important for understanding important concepts, so that one can understand the important design concepts of neural architectures in different applications. Why do neural networks work? When do they work better than off-the-shelf machine-learning models? When is depth useful? Why is training neural networks so hard? What are the pitfalls? The book is also rich in discussing different applications in order to give the practitioner a flavor of how neural architectures are designed for different types of problems. Applications associated with many different areas like recommender systems, machine translation, image captioning, image classification, reinforcement-learning based gaming, and text analytics are covered. The chapters of this book span three categories: The basics of neural networks: Many traditional machine learning models can be understood as special cases of neural networks. An emphasis is placed in the first two chapters on understanding the relationship between traditional machine learning and neural networks. Support vector machines, linear/logistic regression, singular value decomposition, matrix factorization, and recommender systems are shown to be special cases of neural networks. These methods are studied together with recent feature engineering methods like word2vec. Fundamentals of neural networks: A detailed discussion of training and regularization is provided in Chapters 3 and 4. Chapters 5 and 6 present radial-basis function (RBF) networks and restricted Boltzmann machines. Advanced topics in neural networks: Chapters 7 and 8 discuss recurrent neural networks and convolutional neural networks. Several advanced topics like deep reinforcement learning, neural Turing machines, Kohonen self-organizing maps, and generative adversarial networks are introduced in Chapters 9 and 10. The book is written for graduate students, researchers, and practitioners. Numerous exercises are available along with a solution manual to aid in classroom teaching. Where possible, an application-centric view is highlighted in order to provide an understanding of the practical uses of each class of techniques.

Book Deep Learning for Natural Language Processing

Download or read book Deep Learning for Natural Language Processing written by Palash Goyal and published by Apress. This book was released on 2018-06-26 with total page 290 pages. Available in PDF, EPUB and Kindle. Book excerpt: Discover the concepts of deep learning used for natural language processing (NLP), with full-fledged examples of neural network models such as recurrent neural networks, long short-term memory networks, and sequence-2-sequence models. You’ll start by covering the mathematical prerequisites and the fundamentals of deep learning and NLP with practical examples. The first three chapters of the book cover the basics of NLP, starting with word-vector representation before moving onto advanced algorithms. The final chapters focus entirely on implementation, and deal with sophisticated architectures such as RNN, LSTM, and Seq2seq, using Python tools: TensorFlow, and Keras. Deep Learning for Natural Language Processing follows a progressive approach and combines all the knowledge you have gained to build a question-answer chatbot system. This book is a good starting point for people who want to get started in deep learning for NLP. All the code presented in the book will be available in the form of IPython notebooks and scripts, which allow you to try out the examples and extend them in interesting ways. What You Will Learn Gain the fundamentals of deep learning and its mathematical prerequisites Discover deep learning frameworks in Python Develop a chatbot Implement a research paper on sentiment classification Who This Book Is For Software developers who are curious to try out deep learning with NLP.

Book Advanced Methods in Neural Computing

Download or read book Advanced Methods in Neural Computing written by Philip D. Wasserman and published by Van Nostrand Reinhold Company. This book was released on 1993 with total page 280 pages. Available in PDF, EPUB and Kindle. Book excerpt: This is the engineer's guide to artificial neural networks, the advanced computing innovation which is posed to sweep into the world of business and industry. The author presents the basic principles and advanced concepts by means of high-performance paradigms which function effectively in real-world situations.

Book Neural  Novel   Hybrid Algorithms for Time Series Prediction

Download or read book Neural Novel Hybrid Algorithms for Time Series Prediction written by Timothy Masters and published by . This book was released on 1995-10-20 with total page 548 pages. Available in PDF, EPUB and Kindle. Book excerpt: An authoritative guide to predicting the future using neural, novel, and hybrid algorithms Expert Timothy Masters provides you with carefully paced, step-by-step advice and guidance plus the proven tools and techniques you need to develop successful applications for business forecasting, stock market prediction, engineering process control, economic cycle tracking, marketing analysis, and more. Neural, Novel & Hybrid Algorithms for Time Series Prediction provides information on: * Robust confidence intervals for predictions made with neural, ARIMA, and other models * Wavelets for detecting features that presage important events * Multivariate ARMA models for simultaneous prediction of multiple series based on multiple inputs and shocks * Hybrid ARMA/neural models to improve the accuracy of predictions * Data reduction and orthogonalization using principal components and related operations * Digital filters for preprocessing to enhance useful information and suppress noise * Diagnostic tools such as the maximum entropy spectrum and Savitzky-Golay filters for suggesting and validating prediction models * Effective preprocessing techniques for prediction with neural networks CD-ROM INCLUDES: * PREDICT-both DOS and Windows NT versions-a powerful time series program that can be easily customized to make accurate predictions in any application area * Much useful source code, including the complex-general multivariate fast Fourier transform in both C++ and Pentium-optimized assembler

Book Neural Networks for Pattern Recognition

Download or read book Neural Networks for Pattern Recognition written by Christopher M. Bishop and published by Oxford University Press. This book was released on 1995-11-23 with total page 501 pages. Available in PDF, EPUB and Kindle. Book excerpt: Statistical pattern recognition; Probability density estimation; Single-layer networks; The multi-layer perceptron; Radial basis functions; Error functions; Parameter optimization algorithms; Pre-processing and feature extraction; Learning and generalization; Bayesian techniques; Appendix; References; Index.

Book Advanced Applied Deep Learning

Download or read book Advanced Applied Deep Learning written by Umberto Michelucci and published by Apress. This book was released on 2019-09-28 with total page 294 pages. Available in PDF, EPUB and Kindle. Book excerpt: Develop and optimize deep learning models with advanced architectures. This book teaches you the intricate details and subtleties of the algorithms that are at the core of convolutional neural networks. In Advanced Applied Deep Learning, you will study advanced topics on CNN and object detection using Keras and TensorFlow. Along the way, you will look at the fundamental operations in CNN, such as convolution and pooling, and then look at more advanced architectures such as inception networks, resnets, and many more. While the book discusses theoretical topics, you will discover how to work efficiently with Keras with many tricks and tips, including how to customize logging in Keras with custom callback classes, what is eager execution, and how to use it in your models. Finally, you will study how object detection works, and build a complete implementation of the YOLO (you only look once) algorithm in Keras and TensorFlow. By the end of the book you will have implemented various models in Keras and learned many advanced tricks that will bring your skills to the next level. What You Will Learn See how convolutional neural networks and object detection workSave weights and models on diskPause training and restart it at a later stage Use hardware acceleration (GPUs) in your codeWork with the Dataset TensorFlow abstraction and use pre-trained models and transfer learningRemove and add layers to pre-trained networks to adapt them to your specific projectApply pre-trained models such as Alexnet and VGG16 to new datasets Who This Book Is For Scientists and researchers with intermediate-to-advanced Python and machine learning know-how. Additionally, intermediate knowledge of Keras and TensorFlow is expected.

Book Pattern Recognition Using Neural Networks

Download or read book Pattern Recognition Using Neural Networks written by Carl G. Looney and published by Oxford University Press on Demand. This book was released on 1997 with total page 458 pages. Available in PDF, EPUB and Kindle. Book excerpt: Pattern recognizers evolve across the sections into perceptrons, a layer of perceptrons, multiple-layered perceptrons, functional link nets, and radial basis function networks. Other networks covered in the process are learning vector quantization networks, self-organizing maps, and recursive neural networks. Backpropagation is derived in complete detail for one and two hidden layers for both unipolar and bipolar sigmoid activation functions.

Book Understanding Machine Learning

Download or read book Understanding Machine Learning written by Shai Shalev-Shwartz and published by Cambridge University Press. This book was released on 2014-05-19 with total page 415 pages. Available in PDF, EPUB and Kindle. Book excerpt: Introduces machine learning and its algorithmic paradigms, explaining the principles behind automated learning approaches and the considerations underlying their usage.

Book Static and Dynamic Neural Networks

Download or read book Static and Dynamic Neural Networks written by Madan Gupta and published by John Wiley & Sons. This book was released on 2004-04-05 with total page 752 pages. Available in PDF, EPUB and Kindle. Book excerpt: Neuronale Netze haben sich in vielen Bereichen der Informatik und künstlichen Intelligenz, der Robotik, Prozeßsteuerung und Entscheidungsfindung bewährt. Um solche Netze für immer komplexere Aufgaben entwickeln zu können, benötigen Sie solide Kenntnisse der Theorie statischer und dynamischer neuronaler Netze. Aneignen können Sie sie sich mit diesem Lehrbuch! Alle theoretischen Konzepte sind in anschaulicher Weise mit praktischen Anwendungen verknüpft. Am Ende jedes Kapitels können Sie Ihren Wissensstand anhand von Übungsaufgaben überprüfen.

Book Fundamentals of Neural Networks

Download or read book Fundamentals of Neural Networks written by Fausett and published by Prentice Hall. This book was released on 1994 with total page 300 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Book Advanced Algorithms and Data Structures

Download or read book Advanced Algorithms and Data Structures written by Marcello La Rocca and published by Simon and Schuster. This book was released on 2021-08-10 with total page 768 pages. Available in PDF, EPUB and Kindle. Book excerpt: Advanced Algorithms and Data Structures introduces a collection of algorithms for complex programming challenges in data analysis, machine learning, and graph computing. Summary As a software engineer, you’ll encounter countless programming challenges that initially seem confusing, difficult, or even impossible. Don’t despair! Many of these “new” problems already have well-established solutions. Advanced Algorithms and Data Structures teaches you powerful approaches to a wide range of tricky coding challenges that you can adapt and apply to your own applications. Providing a balanced blend of classic, advanced, and new algorithms, this practical guide upgrades your programming toolbox with new perspectives and hands-on techniques. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology Can you improve the speed and efficiency of your applications without investing in new hardware? Well, yes, you can: Innovations in algorithms and data structures have led to huge advances in application performance. Pick up this book to discover a collection of advanced algorithms that will make you a more effective developer. About the book Advanced Algorithms and Data Structures introduces a collection of algorithms for complex programming challenges in data analysis, machine learning, and graph computing. You’ll discover cutting-edge approaches to a variety of tricky scenarios. You’ll even learn to design your own data structures for projects that require a custom solution. What's inside Build on basic data structures you already know Profile your algorithms to speed up application Store and query strings efficiently Distribute clustering algorithms with MapReduce Solve logistics problems using graphs and optimization algorithms About the reader For intermediate programmers. About the author Marcello La Rocca is a research scientist and a full-stack engineer. His focus is on optimization algorithms, genetic algorithms, machine learning, and quantum computing. Table of Contents 1 Introducing data structures PART 1 IMPROVING OVER BASIC DATA STRUCTURES 2 Improving priority queues: d-way heaps 3 Treaps: Using randomization to balance binary search trees 4 Bloom filters: Reducing the memory for tracking content 5 Disjoint sets: Sub-linear time processing 6 Trie, radix trie: Efficient string search 7 Use case: LRU cache PART 2 MULTIDEMENSIONAL QUERIES 8 Nearest neighbors search 9 K-d trees: Multidimensional data indexing 10 Similarity Search Trees: Approximate nearest neighbors search for image retrieval 11 Applications of nearest neighbor search 12 Clustering 13 Parallel clustering: MapReduce and canopy clustering PART 3 PLANAR GRAPHS AND MINIMUM CROSSING NUMBER 14 An introduction to graphs: Finding paths of minimum distance 15 Graph embeddings and planarity: Drawing graphs with minimal edge intersections 16 Gradient descent: Optimization problems (not just) on graphs 17 Simulated annealing: Optimization beyond local minima 18 Genetic algorithms: Biologically inspired, fast-converging optimization

Book Algorithms

    Book Details:
  • Author : Rob Botwright
  • Publisher : Rob Botwright
  • Release : 101-01-01
  • ISBN : 1839386193
  • Pages : 286 pages

Download or read book Algorithms written by Rob Botwright and published by Rob Botwright. This book was released on 101-01-01 with total page 286 pages. Available in PDF, EPUB and Kindle. Book excerpt: Introducing "ALGORITHMS: COMPUTER SCIENCE UNVEILED" - Your Path to Algorithmic Mastery! Are you fascinated by the world of computer science and the magic of algorithms? Do you want to unlock the power of algorithmic thinking and take your skills to expert levels? Look no further! This exclusive book bundle is your comprehensive guide to mastering the art of algorithms and conquering the exciting realm of computer science. 📘 BOOK 1 - COMPUTER SCIENCE: ALGORITHMS UNVEILED 📘 · Dive into the fundamentals of algorithms. · Perfect for beginners and those new to computer science. · Learn the building blocks of algorithmic thinking. · Lay a strong foundation for your journey into the world of algorithms. 📘 BOOK 2 - MASTERING ALGORITHMS: FROM BASICS TO EXPERT LEVEL 📘 · Take your algorithmic skills to new heights. · Explore advanced sorting and searching techniques. · Uncover the power of dynamic programming and greedy algorithms. · Ideal for students and professionals looking to become algorithmic experts. 📘 BOOK 3 - ALGORITHMIC MASTERY: A JOURNEY FROM NOVICE TO GURU 📘 · Embark on a transformative journey from novice to guru. · Master divide and conquer strategies. · Discover advanced data structures and their applications. · Tackle algorithmic challenges that demand mastery. · Suitable for anyone seeking to elevate their problem-solving abilities. 📘 BOOK 4 - ALGORITHMIC WIZARDRY: UNRAVELING COMPLEXITY FOR EXPERTS 📘 · Push the boundaries of your algorithmic expertise. · Explore expert-level techniques and conquer puzzles. · Unleash the full power of algorithmic mastery. · For those who aspire to become true algorithmic wizards. Why Choose "ALGORITHMS: COMPUTER SCIENCE UNVEILED"? 🌟 Comprehensive Learning: Covering the entire spectrum of algorithmic knowledge, this bundle caters to beginners and experts alike. 🌟 Progression: Start with the basics and gradually advance to expert-level techniques, making it accessible for learners at all stages. 🌟 Real-World Application: Gain practical skills and problem-solving abilities that are highly sought after in the world of computer science. 🌟 Expert Authors: Written by experts in the field, each book provides clear explanations and hands-on examples. 🌟 Career Advancement: Enhance your career prospects with a deep understanding of algorithms, an essential skill in today's tech-driven world. Unlock the Secrets of Computer Science Today! Whether you're a student, a professional, or simply curious about computer science, "ALGORITHMS: COMPUTER SCIENCE UNVEILED" is your gateway to a world of knowledge and expertise. Don't miss this opportunity to acquire a valuable skill set that can propel your career to new heights. Get your copy now and embark on a journey to algorithmic mastery!

Book Machine Learning Foundations

Download or read book Machine Learning Foundations written by Taeho Jo and published by Springer Nature. This book was released on 2021-02-12 with total page 391 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides conceptual understanding of machine learning algorithms though supervised, unsupervised, and advanced learning techniques. The book consists of four parts: foundation, supervised learning, unsupervised learning, and advanced learning. The first part provides the fundamental materials, background, and simple machine learning algorithms, as the preparation for studying machine learning algorithms. The second and the third parts provide understanding of the supervised learning algorithms and the unsupervised learning algorithms as the core parts. The last part provides advanced machine learning algorithms: ensemble learning, semi-supervised learning, temporal learning, and reinforced learning. Provides comprehensive coverage of both learning algorithms: supervised and unsupervised learning; Outlines the computation paradigm for solving classification, regression, and clustering; Features essential techniques for building the a new generation of machine learning.