Non-fully Configured Second-order Neural Networks Using Multi-dimensional Weights PDF Download
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Author: Yong-Chul Shin
Publisher:
ISBN:
Category :
Languages : en
Pages : 284
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Book Description
Author: Yong-Chul Shin
Publisher:
ISBN:
Category :
Languages : en
Pages : 284
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Book Description
Author: Patrick K. Simpson
Publisher: Institute of Electrical & Electronics Engineers(IEEE)
ISBN:
Category : Computers
Languages : en
Pages : 984
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Book Description
Author:
Publisher:
ISBN:
Category : Dissertations, Academic
Languages : en
Pages : 854
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Book Description
Author:
Publisher: Psychology Press
ISBN: 9780805814972
Category : Computer science
Languages : en
Pages : 744
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Book Description
Author: Bharath Ramsundar
Publisher: "O'Reilly Media, Inc."
ISBN: 1491980400
Category : Computers
Languages : en
Pages : 247
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Book Description
Learn how to solve challenging machine learning problems with TensorFlow, Google’s revolutionary new software library for deep learning. If you have some background in basic linear algebra and calculus, this practical book introduces machine-learning fundamentals by showing you how to design systems capable of detecting objects in images, understanding text, analyzing video, and predicting the properties of potential medicines. TensorFlow for Deep Learning teaches concepts through practical examples and helps you build knowledge of deep learning foundations from the ground up. It’s ideal for practicing developers with experience designing software systems, and useful for scientists and other professionals familiar with scripting but not necessarily with designing learning algorithms. Learn TensorFlow fundamentals, including how to perform basic computation Build simple learning systems to understand their mathematical foundations Dive into fully connected deep networks used in thousands of applications Turn prototypes into high-quality models with hyperparameter optimization Process images with convolutional neural networks Handle natural language datasets with recurrent neural networks Use reinforcement learning to solve games such as tic-tac-toe Train deep networks with hardware including GPUs and tensor processing units
Author: Nikhil Buduma
Publisher: "O'Reilly Media, Inc."
ISBN: 1491925566
Category : Computers
Languages : en
Pages : 272
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Book Description
With the reinvigoration of neural networks in the 2000s, deep learning has become an extremely active area of research, one that’s paving the way for modern machine learning. In this practical book, author Nikhil Buduma provides examples and clear explanations to guide you through major concepts of this complicated field. Companies such as Google, Microsoft, and Facebook are actively growing in-house deep-learning teams. For the rest of us, however, deep learning is still a pretty complex and difficult subject to grasp. If you’re familiar with Python, and have a background in calculus, along with a basic understanding of machine learning, this book will get you started. Examine the foundations of machine learning and neural networks Learn how to train feed-forward neural networks Use TensorFlow to implement your first neural network Manage problems that arise as you begin to make networks deeper Build neural networks that analyze complex images Perform effective dimensionality reduction using autoencoders Dive deep into sequence analysis to examine language Learn the fundamentals of reinforcement learning
Author: The International Neural Society(INNS), The IEEE Neural
Publisher: Springer Science & Business Media
ISBN: 9400906439
Category : Computers
Languages : en
Pages : 569
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Book Description
Neural Networks have been the theater of a dramatic increase of activities in the last five years. The interest of mixing results from fields as different as neurobiology, physics (spin glass theory), mathematics (linear algebra, statistics ... ), computer science (software engineering, hardware architectures ... ) or psychology has attracted a large number of researchers to the field. The perspective of dramatic improvements in many applications has lead important companies to launch new neural network programs and start-ups have mushroomed to address this new market. Throughout the world large programs are being set-up: in Japan the government has committed more than $18 million per year to its 20 year Human Frontier Science program; the DARPA and the US Navy have alloted more than $10 million per year each and other US government agencies are contributing to important but less ambitious programs. Neural networks are also a major research are in the supercomputing initiative. Europe has from the beginning taken an active part in funding major projects in the new field with BRAIN, BRA, ANNIE and PYGMALION (Esprit). Approximately $20 million has been invested to date since 1988 and new programs of nearly $30 million are being funded for the next 3 years. National projects in certain countries may globally double these amounts. Neural network conferences are attracting larger audiences than ever before. Prior to 1987 attendance never surpassed 300. The June 1989 IJCNN conference in Washington had over 2200 participants.
Author: E. Agazzi
Publisher: Springer Science & Business Media
ISBN: 9401593914
Category : Science
Languages : en
Pages : 368
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Book Description
Observability and Scientific Realism It is commonly thought that the birth of modern natural science was made possible by an intellectual shift from a mainly abstract and specuJative conception of the world to a carefully elaborated image based on observations. There is some grain of truth in this claim, but this grain depends very much on what one takes observation to be. In the philosophy of science of our century, observation has been practically equated with sense perception. This is understandable if we think of the attitude of radical empiricism that inspired Ernst Mach and the philosophers of the Vienna Circle, who powerfully influenced our century's philosophy of science. However, this was not the atti tude of the f ounders of modern science: Galileo, f or example, expressed in a f amous passage of the Assayer the conviction that perceptual features of the world are merely subjective, and are produced in the 'anima!' by the motion and impacts of unobservable particles that are endowed uniquely with mathematically expressible properties, and which are therefore the real features of the world. Moreover, on other occasions, when defending the Copernican theory, he explicitly remarked that in admitting that the Sun is static and the Earth turns on its own axis, 'reason must do violence to the sense' , and that it is thanks to this violence that one can know the tme constitution of the universe.
Author: Rudolf F. Albrecht
Publisher: Springer Science & Business Media
ISBN: 370917533X
Category : Computers
Languages : en
Pages : 752
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Book Description
Artificial neural networks and genetic algorithms both are areas of research which have their origins in mathematical models constructed in order to gain understanding of important natural processes. By focussing on the process models rather than the processes themselves, significant new computational techniques have evolved which have found application in a large number of diverse fields. This diversity is reflected in the topics which are the subjects of contributions to this volume. There are contributions reporting theoretical developments in the design of neural networks, and in the management of their learning. In a number of contributions, applications to speech recognition tasks, control of industrial processes as well as to credit scoring, and so on, are reflected. Regarding genetic algorithms, several methodological papers consider how genetic algorithms can be improved using an experimental approach, as well as by hybridizing with other useful techniques such as tabu search. The closely related area of classifier systems also receives a significant amount of coverage, aiming at better ways for their implementation. Further, while there are many contributions which explore ways in which genetic algorithms can be applied to real problems, nearly all involve some understanding of the context in order to apply the genetic algorithm paradigm more successfully. That this can indeed be done is evidenced by the range of applications covered in this volume.
Author: Osval Antonio Montesinos López
Publisher: Springer Nature
ISBN: 3030890104
Category : Technology & Engineering
Languages : en
Pages : 707
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Book Description
This book is open access under a CC BY 4.0 license This open access book brings together the latest genome base prediction models currently being used by statisticians, breeders and data scientists. It provides an accessible way to understand the theory behind each statistical learning tool, the required pre-processing, the basics of model building, how to train statistical learning methods, the basic R scripts needed to implement each statistical learning tool, and the output of each tool. To do so, for each tool the book provides background theory, some elements of the R statistical software for its implementation, the conceptual underpinnings, and at least two illustrative examples with data from real-world genomic selection experiments. Lastly, worked-out examples help readers check their own comprehension.The book will greatly appeal to readers in plant (and animal) breeding, geneticists and statisticians, as it provides in a very accessible way the necessary theory, the appropriate R code, and illustrative examples for a complete understanding of each statistical learning tool. In addition, it weighs the advantages and disadvantages of each tool.
