Unlock deeper insights into machine learning with this vital guide to cutting-edge predictive analytics
標(biāo)簽： Learning Machine Python
上傳時間： 2017-10-27
上傳用戶：shawnleaves
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標(biāo)簽： 源代碼
上傳時間： 2017-11-17
上傳用戶：wendingchang
DEEP learning paper DEEP learning paper DEEP learning paper DEEP learning paper DEEP learning paper DEEP learning paper DEEP learning paper DEEP learning paper
上傳時間： 2018-06-13
上傳用戶：1203955829@qq.com
This book is an entry-level text on the technology of telecommunications. It has been crafted with the newcomer in mind. The eighteen chapters of text have been prepared for high-school graduates who understand algebra, logarithms, and basic electrical prin- ciples such as Ohm’s law. However, many users require support in these areas so Appen- dices A and B review the essentials of electricity and mathematics through logarithms.
標(biāo)簽： Telecommunications Fundamentals 1st of ed
上傳時間： 2020-05-27
上傳用戶：shancjb
This book is an entry-level text on the technology of telecommunications. It has been crafted with the newcomer in mind. The twenty-one chapters of text have been prepared for high-school graduates who understand algebra, logarithms, and the basic principles of electricity such as Ohm’s law. However, it is appreciated that many readers require support in these areas. Appendices A and B review the essentials of electricity and mathematics up through logarithms. This material was placed in the appendices so as not to distract from the main theme, the technology of telecommunication systems. Another topic that many in the industry find difficult is the use of decibels and derived units. Appendix C provides the reader a basic understanding of decibels and their applications. The only mathematics necessary is an understanding of the powers of ten
標(biāo)簽： Telecommunications Fundamentals 2nd of ed
上傳時間： 2020-05-27
上傳用戶：shancjb
MIMO-OFDM is a key technology for next-generation cellular communications (3GPP-LTE, Mobile WiMAX, IMT-Advanced) as well as wireless LAN (IEEE 802.11a, IEEE 802.11n), wireless PAN (MB-OFDM), and broadcasting (DAB, DVB, DMB). This book provides a comprehensive introduction to the basic theory and practice of wireless channel modeling, OFDM, and MIMO, with MATLAB ? programs to simulate the underlying techniques on MIMO-OFDMsystems.Thisbookisprimarilydesignedforengineersandresearcherswhoare interested in learning various MIMO-OFDM techniques and applying them to wireless communications.
標(biāo)簽： Communications MIMO-OFDM Wireless MATLAB with
上傳時間： 2020-05-28
上傳用戶：shancjb
This edition updates and continues the series of books based on the residential courses on radiowave propagation organised by the IEE/IET. The first course was held in 1974, with lectures by H. Page, P. Matthews, D. Parsons, M.W. Gough, P.A. Watson, E. Hickin, T. Pratt, P. Knight, T.B. Jones, P.A. Bradley, B. Burgess and H. Rishbeth.
標(biāo)簽： Propagation Radiowaves edition 3rd of
上傳時間： 2020-05-31
上傳用戶：shancjb
Although state of the art in many typical machine learning tasks, deep learning algorithmsareverycostly interms ofenergyconsumption,duetotheirlargeamount of required computations and huge model sizes. Because of this, deep learning applications on battery-constrained wearables have only been possible through wireless connections with a resourceful cloud. This setup has several drawbacks. First, there are privacy concerns. Cloud computing requires users to share their raw data—images, video, locations, speech—with a remote system. Most users are not willing to do this. Second, the cloud-setup requires users to be connected all the time, which is unfeasible given current cellular coverage. Furthermore, real-time applications require low latency connections, which cannot be guaranteed using the current communication infrastructure. Finally, wireless connections are very inefficient—requiringtoo much energyper transferredbit for real-time data transfer on energy-constrained platforms.
標(biāo)簽： Embedded_Deep_Learning Algorithms
上傳時間： 2020-06-10
上傳用戶：shancjb
Machinelearninghasgreatpotentialforimprovingproducts,processesandresearch.Butcomputers usually do not explain their predictions which is a barrier to the adoption of machine learning. This book is about making machine learning models and their decisions interpretable. After exploring the concepts of interpretability, you will learn about simple, interpretable models such as decision trees, decision rules and linear regression. Later chapters focus on general model- agnosticmethodsforinterpretingblackboxmodelslikefeatureimportanceandaccumulatedlocal effects and explaining individual predictions with Shapley values and LIME.
標(biāo)簽： interpretable-machine-learning
上傳時間： 2020-06-10
上傳用戶：shancjb
Much has been written concerning the manner in which healthcare is changing, with a particular emphasis on how very large quantities of data are now being routinely collected during the routine care of patients. The use of machine learning meth- ods to turn these ever-growing quantities of data into interventions that can improve patient outcomes seems as if it should be an obvious path to take. However, the field of machine learning in healthcare is still in its infancy. This book, kindly supported by the Institution of Engineering andTechnology, aims to provide a “snap- shot” of the state of current research at the interface between machine learning and healthcare.
標(biāo)簽： Technologies Healthcare Learning Machine
上傳時間： 2020-06-10
上傳用戶：shancjb
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