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 Free Download Basic Image Creation with Diffusion Neural Networks Published 4/2024 MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch Language: English | Duration: 1h 21m | Size: 585 MB Mastering Image Creation with Diffusion Neural Networks  Free Download Vector Neural Networks: With Geometric Tensors by Scott D. Seto English | March 28, 2024 | ASIN: B0CXBV3DY5 | 329 pages | PDF | 145 Mb
 Free Download Neural Networks with Python: Design CNNs, Transformers, GANs and capsule networks using tensorflow and keras by Mei Wong English | November 2, 2023 | ISBN: 8119177487 | 150 pages | PDF | 2.22 Mb "Neural Networks with Python" serves as an introductory guide for those taking their first steps into neural network development with Python. It's tailored to assist beginners in understanding the foundational elements of neural networks and to provide them with the confidence to delve deeper into this intriguing area of machine learning.  Free Download Graph Neural Networks in Action (MEAP V08) + Code by English | 2023 | ISBN: 9781617299056 | 404 pages | MOBI + Code | 45 Mb A hands-on guide to powerful graph-based deep learning models! Learn how to build cutting-edge graph neural networks for recommendation engines, molecular modeling, and more.  Free Download Dynamics of Small Neural Populations By John Milton 1996 | 125 Pages | ISBN: 0821804588 | PDF | 22 MB This book arose from a series of lectures presented at the CRM Summer School in Mathematical Biology held at the University of British Columbia in the summer of 1993 by John Milton, a clinical neurologist and biomathematician. In this work, three themes are explored: time-delayed feedback control, noise, and statistical properties of neurons and large neural populations. This volume focuses on systems composed of 2-3 neurons. Such neural populations are small enough to permit experimental manipulation while at the same time are well enough characterized so that plausible mathematical models can be posed. Thus, direct comparisons between theory and observation are in principle possible. Features: First text to review the effects of time delays and noise on neural control. Accessible to both neurobiologists and mathematicians. Emphasis on problems for which comparisons between observation and prediction are possible. Self-contained and succinctly written. Excellent source for potential research topics.  Free Download Genetic Algorithms & Neural Networks A Practical Approach Published 4/2024 Created by Catalin Baba MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch Genre: eLearning | Language: English | Duration: 28 Lectures ( 6h 8m ) | Size: 1.75 GB  Free Download Convergence Analysis of Recurrent Neural Networks by Zhang Yi , K. K. Tan English | PDF | 2004 | 244 Pages | ISBN : 1402076940 | 8.5 MB Since the outstanding and pioneering research work of Hopfield on recurrent neural networks (RNNs) in the early 80s of the last century, neural networks have rekindled strong interests in scientists and researchers. Recent years have recorded a remarkable advance in research and development work on RNNs, both in theoretical research as weIl as actual applications. The field of RNNs is now transforming into a complete and independent subject. From theory to application, from software to hardware, new and exciting results are emerging day after day, reflecting the keen interest RNNs have instilled in everyone, from researchers to practitioners. RNNs contain feedback connections among the neurons, a phenomenon which has led rather naturally to RNNs being regarded as dynamical systems. RNNs can be described by continuous time differential systems, discrete time systems, or functional differential systems, and more generally, in terms of non linear systems. Thus, RNNs have to their disposal, a huge set of mathematical tools relating to dynamical system theory which has tumed out to be very useful in enabling a rigorous analysis of RNNs.  Free Download Building Neural Networks By David M. Skapura 1995 | 304 Pages | ISBN: 0201539217 | DJVU | 16 MB Neural network theory can be learned with relative ease, yet learning to apply the technology successfully can be a slow, trial-and-error process. In this study, the connectionist model is taught using numerous examples that show how people have built neural network applications."This practical introduction describes the kinds of real-world problems neural network technology can solve. Surveying a range of neural network applications, the book demonstrates the construction and operation of artificial neural systems. Through numerous examples, the author explains the process of building neural-network applications that utilize recent connectionist developments, and conveys an understanding both of the potential, and the limitations of different network models. Examples are described in enough detail for you to assimilate the information and then use the accumulated experience of others to create your own applications. These examples are deliberately restricted to those that can be easily understood, and recreated, by any reader, even the novice practitioner. In some cases the author describes alternative approaches to the same application, to allow you to compare and contrast their advantages and disadvantages.Organized by application areas, rather than by specific network architectures or learning algorithms, Builiding Neural Networks shows why certain networks are more suitable than others for solving specific kinds of problems. Skapura also reviews principles of neural information processing and furnishes an operations summary of the most popular neural-network processing models. Finally, the book provides information on the practical aspects of application design, and contains six topic-oriented chapters on specific applications of neural-network systems. These applications include networks that perform: Pattern matching, storage, and recall Business and financial systems Data extraction from images Mechanical process control systems New neural networks that combine pattern matching with fuzzy logicThe book includes application-oriented excercises that further help you see how a neural network solves a problem, and that reinforce your understanding of modeling techniques."" David M. Skapura is the coauthor, with James A. Freeman, of Neural Networks: Algorithms, Applications, and Programming Techniques. He is currently employed by Brightware Corporation (a spin-off of Inference Corporation), where he works as an applications consultant, developing customized knowledge-based systems and applications. He is also the founder and president of Scient Computing, a small Houston consulting firm specializing in neural-networking applications and research. Previously at Loral Space Information Systems, Skapura investigated the applicability of neural networks to NASA's advanced automation requirements. He is an adjunct professor at the University of Houston at Clear Lake."  Free Download Ranjodh Singh Dhaliwal, "Neural Networks " English | ISBN: 1517916690 | 2024 | 122 pages | EPUB | 489 KB A critical examination of the figure of the neural network as it mediates neuroscientific and computational discourses and technical practices  Free Download Neural Computers by Rolf Eckmiller, Christoph Malsburg English | PDF | 1989 | 562 Pages | ISBN : 3540508929 | 46.6 MB the outcome of a NATO Advanced Research Workshop (ARW) This book is held in Neuss (near Dusseldorf), Federal Republic of Germany from 28 September to 2 October, 1987. The workshop assembled some 50 invited experts from Europe, Ameri ca, and Japan representing the fields of Neuroscience, Computational Neuroscience, Cellular Automata, Artificial Intelligence, and Compu ter Design; more than 20 additional scientists from various countries attended as observers. The 50 contributions in this book cover a wide range of topics, including: Neural Network Architecture, Learning and Memory, Fault Tolerance, Pattern Recognition, and Motor Control in Brains versus Neural Computers. Twelve of these contributions are review papers. The readability of this book was enhanced by a number of measures: * The contributions are arranged in seven chapters. * A separate List of General References helps newcomers to this ra pidly growing field to find introductory books. * The Collection of References from all Contributions provides an alphabetical list of all references quoted in the individual con tributions. * Separate Reference Author and Subject Indices facilitate access to various details. Group Reports (following the seven chapters) summarize the discus sions regarding four specific topics relevant for the 'state of the art' in Neural Computers. |
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