UNDERSTANDING MACHINE LEARNING

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Understanding Machine Learning

Author : Shai Shalev-Shwartz
ISBN : 9781107057135
Genre : Computers
File Size : 54.1 MB
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Introduces machine learning and its algorithmic paradigms, explaining the principles behind automated learning approaches and the considerations underlying their usage.
Category: Computers

Python Machine Learning A Practical Beginner S Guide To Understanding Machine Learning Deep Learning And Neural Networks With Python Scikit Learn Tensorflow And Keras

Author : Brandon Railey
ISBN : 9781386372585
Genre : Computers
File Size : 69.72 MB
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★☆Have you come across the terms machine learning and neural networks in most articles you have recently read? Do you also want to learn how to build a machine learning model that will answer your questions within a blink of your eyes?☆★ If you responded yes to any of the above questions, you have come to the right place. Machine learning is an incredibly dense topic. It's hard to imagine condensing it into an easily readable and digestible format. However, this book aims to do exactly that. Machine learning and artificial intelligence have been used in different machines and applications to improve the user's experience. One can also use machine learning to make data analysis and predicting the output for some data sets easy. All you need to do is choose the right algorithm, train the model and test the model before you apply it on any real-world tool. It is that simple isn't it? ★★Apart from this, you will also learn more about★★ ♦ The Different Types Of Learning Algorithm That You Can Expect To Encounter ♦ The Numerous Applications Of Machine Learning And Deep Learning ♦ The Best Practices For Picking Up Neural Networks ♦ What Are The Best Languages And Libraries To Work With ♦ The Various Problems That You Can Solve With Machine Learning Algorithms ♦ And much more... Well, you can do it faster if you use Python. This language has made it easy for any user, even an amateur, to build a strong machine learning model since it has numerous directories and libraries that make it easy for one to build a model. Do you want to know how to build a machine learning model and a neural network? So, what are you waiting for? Grab a copy of this book now!
Category: Computers

Understanding And Interpreting Machine Learning In Medical Image Computing Applications

Author : Danail Stoyanov
ISBN : 9783030026288
Genre : Computers
File Size : 84.35 MB
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This book constitutes the refereed joint proceedings of the First International Workshop on Machine Learning in Clinical Neuroimaging, MLCN 2018, the First International Workshop on Deep Learning Fails, DLF 2018, and the First International Workshop on Interpretability of Machine Intelligence in Medical Image Computing, iMIMIC 2018, held in conjunction with the 21st International Conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2018, in Granada, Spain, in September 2018. The 4 full MLCN papers, the 6 full DLF papers, and the 6 full iMIMIC papers included in this volume were carefully reviewed and selected. The MLCN contributions develop state-of-the-art machine learning methods such as spatio-temporal Gaussian process analysis, stochastic variational inference, and deep learning for applications in Alzheimer's disease diagnosis and multi-site neuroimaging data analysis; the DLF papers evaluate the strengths and weaknesses of DL and identify the main challenges in the current state of the art and future directions; the iMIMIC papers cover a large range of topics in the field of interpretability of machine learning in the context of medical image analysis.
Category: Computers

Introduction To Mathematics For Understanding Deep Learning

Author : Kazuyuki FUJII
ISBN : 9781618965448
Genre : Mathematics
File Size : 48.32 MB
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Deep Learning is the heart of Artificial Intelligence and will become a most important field in Data Science in the near future. Deep Learning has attracted much attention recently. It is usually carried out by the gradient descent method, which is not always easy to understand for beginners. When one starts studying Deep Learning first hurdles are (1) how to choose the learning rate (2) how to avoid being trapped by local minima (3) what is a deep meaning of the minibatch. In this book I plan to offer intuitive answers to these questions within my understandings. As a matter of course, when beginners study Deep Learning some mathematical knowledge from Calculus, Linear Algebra, Statistics and Information are required. In the book I gave minimum knowledge required for understanding Deep learning. After reading the book, readers are encouraged to challenge advanced books of Deep Learning (or Artificial Intelligence).
Category: Mathematics

Machine Learning

Author : Sebastian Dark
ISBN : 172382125X
Genre : Computers
File Size : 87.71 MB
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Curious to learn about the revolutionary technology that is shaping our future and changing the world? Machine learning is a part of the field of computer science that involves computer systems being able to "learn" with data despite not being programmed explicitly. In 2017, AlphaGo, which is AI developed by Google DeepMind and started off by only knowing the rules of the game, was eventually able to train itself and beat Ke Jie, the world No.1 ranked player at the time. Although this may not seem that impressive at first, it is important to understand that Go is a very complex game that many programmers were not able to trump with AI in the past. Although Go is an interesting example, the possibilities of using machine learning are limitless. From retail to medicine to finance, machine learning has the ability to change each industry it comes into contact with. In fact, this revolution has already begun and will only continue to get bigger. Without a doubt it is the future. However, it is as complex as it is revolutionary. If you do not have a background or any experience in the field, it is easy to get bogged down by all the complicated concepts and term. Furthermore, finding information that is easy to understand can prove to be a challenge because it most likely will not be thorough even if you do find it. Some of the things you will learn include... What Machine Learning Really is and How It Can Change the World The Fields of Study and Subjects Involved Various Applications of Machine Learning Supervised vs Unsupervised Learning Neural Networks Deep Learning And much more! In this book, you will find the perfect balance between the information being very thorough and being able to understand it. Although tailored for beginners, it won't contain simple and easily accessible information. You will dive deep into machine learning but you will be carefully led through it in a way that will make everything easy to understand even if you do not have a technical background in computer programming. Whether you are looking to gain knowledge for a potential career in machine learning, want to learn how this will impact our future, or simply want to satisfy your curiosity about potentially the greatest technological advancement of our time, this book will help tremendously in understanding machine learning. If you are finally prepared to understand this revolutionary yet complex technology at a high level despite what your technical background may be, Purchase Now! **Get the Kindle eBook version for FREE when you buy the Paperback version of this book!**
Category: Computers

Shape Understanding System Knowledge Implementation And Learning

Author : Zbigniew Les
ISBN : 9783642296970
Genre : Computers
File Size : 28.49 MB
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This book presents the selected results of research on the further development of the shape understanding system (SUS) described in our previous book titled “Shape Understanding System: the First Steps Toward the Visual Thinking Machines”. This is the second book that presents the results of research in the area of thinking and understanding carried out by authors in the newly founded the Queen Jadwiga Research Institute of Understanding. In this book, the new term knowledge implementation is introduced to denote the new method of the meaningful learning in the context of machine understanding. SUS ability to understand is related to the different categories of objects such as the category of visual objects, the category of sensory objects and the category of text objects. In this book, new terms and concepts are introduced in order to describe and explain some issues connected with SUS development. These terms are explained by referring to the content of our books and other our works rather than to existing literature in related areas of research. This book raises many questions that are discussed in the area of cognitive science or philosophy of mind.
Category: Computers

Applied Deep Learning

Author : Umberto Michelucci
ISBN : 9781484237908
Genre : Computers
File Size : 59.24 MB
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Work with advanced topics in deep learning, such as optimization algorithms, hyper-parameter tuning, dropout, and error analysis as well as strategies to address typical problems encountered when training deep neural networks. You’ll begin by studying the activation functions mostly with a single neuron (ReLu, sigmoid, and Swish), seeing how to perform linear and logistic regression using TensorFlow, and choosing the right cost function. The next section talks about more complicated neural network architectures with several layers and neurons and explores the problem of random initialization of weights. An entire chapter is dedicated to a complete overview of neural network error analysis, giving examples of solving problems originating from variance, bias, overfitting, and datasets coming from different distributions. Applied Deep Learning also discusses how to implement logistic regression completely from scratch without using any Python library except NumPy, to let you appreciate how libraries such as TensorFlow allow quick and efficient experiments. Case studies for each method are included to put into practice all theoretical information. You’ll discover tips and tricks for writing optimized Python code (for example vectorizing loops with NumPy). What You Will Learn Implement advanced techniques in the right way in Python and TensorFlow Debug and optimize advanced methods (such as dropout and regularization) Carry out error analysis (to realize if one has a bias problem, a variance problem, a data offset problem, and so on) Set up a machine learning project focused on deep learning on a complex dataset Who This Book Is For Readers with a medium understanding of machine learning, linear algebra, calculus, and basic Python programming.
Category: Computers

Machine Learning And Data Mining In Pattern Recognition

Author : Petra Perner
ISBN : 9783540665991
Genre : Computers
File Size : 40.55 MB
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The field of machine learning and data mining in connection with pattern recognition enjoys growing popularity and attracts many researchers. Automatic pattern recognition systems have proven successful in many applications. The wide use of these systems depends on their ability to adapt to changing environmental conditions and to deal with new objects. This requires learning capabilities on the parts of these systems. The exceptional attraction of learning in pattern recognition lies in the specific data themselves and the different stages at which they get processed in a pattern recognition system. This results a specific branch within the field of machine learning. At the workshop, were presented machine learning approaches for image pre-processing, image segmentation, recognition and interpretation. Machine learning systems were shown on applications such as document analysis and medical image analysis. Many databases are developed that contain multimedia sources such as images, measurement protocols, and text documents. Such systems should be able to retrieve these sources by content. That requires specific retrieval and indexing strategies for images and signals. Higher quality database contents can be achieved if it were possible to mine these databases for their underlying information. Such mining techniques have to consider the specific characteristic of the image sources. The field of mining multimedia databases is just starting out. We hope that our workshop can attract many other researchers to this subject.
Category: Computers

Pattern Recognition And Machine Learning

Author : Y. Anzai
ISBN : 9780080513638
Genre : Computers
File Size : 64.23 MB
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This is the first text to provide a unified and self-contained introduction to visual pattern recognition and machine learning. It is useful as a general introduction to artifical intelligence and knowledge engineering, and no previous knowledge of pattern recognition or machine learning is necessary. Basic for various pattern recognition and machine learning methods. Translated from Japanese, the book also features chapter exercises, keywords, and summaries.
Category: Computers

Machine Learning For Vision Based Motion Analysis

Author : Liang Wang
ISBN : 0857290576
Genre : Computers
File Size : 61.54 MB
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Techniques of vision-based motion analysis aim to detect, track, identify, and generally understand the behavior of objects in image sequences. With the growth of video data in a wide range of applications from visual surveillance to human-machine interfaces, the ability to automatically analyze and understand object motions from video footage is of increasing importance. Among the latest developments in this field is the application of statistical machine learning algorithms for object tracking, activity modeling, and recognition. Developed from expert contributions to the first and second International Workshop on Machine Learning for Vision-Based Motion Analysis, this important text/reference highlights the latest algorithms and systems for robust and effective vision-based motion understanding from a machine learning perspective. Highlighting the benefits of collaboration between the communities of object motion understanding and machine learning, the book discusses the most active forefronts of research, including current challenges and potential future directions. Topics and features: provides a comprehensive review of the latest developments in vision-based motion analysis, presenting numerous case studies on state-of-the-art learning algorithms; examines algorithms for clustering and segmentation, and manifold learning for dynamical models; describes the theory behind mixed-state statistical models, with a focus on mixed-state Markov models that take into account spatial and temporal interaction; discusses object tracking in surveillance image streams, discriminative multiple target tracking, and guidewire tracking in fluoroscopy; explores issues of modeling for saliency detection, human gait modeling, modeling of extremely crowded scenes, and behavior modeling from video surveillance data; investigates methods for automatic recognition of gestures in Sign Language, and human action recognition from small training sets. Researchers, professional engineers, and graduate students in computer vision, pattern recognition and machine learning, will all find this text an accessible survey of machine learning techniques for vision-based motion analysis. The book will also be of interest to all who work with specific vision applications, such as surveillance, sport event analysis, healthcare, video conferencing, and motion video indexing and retrieval.
Category: Computers