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: British Army Gun Tractors (Military Trucks Archive 9)
: Kelsey Publishing
: 2022
: True PDF
: 100
: 16 Mb
: English

British Army Gun Tractors (Military Trucks Archive 9)
: dimarik , 04:26 | : 0 |
Using XLWings, BeautifulSoup, Python 3.9 and Microsoft Excel 365 VBA To Web Scrape Market Index Data and Historical Company Earnings: Using XLWings, BeautifulSoup, Python 3.9 and Microsoft Excel 365 VBA To Web Scrape Market Index Data and Historical Company Earnings
: John Lee
: John Lee
: January 01,2022
: 94
:
: epub
: 10.1 MB

In this book, we will use Python to retrieve market index data and historical company earnings We will use Microsoft Excel 365 to report the market index and company earnings. We will use a Python library called Xlwings so Excel 365 can communicate with Python. The target audience for this book is for people who are interested in getting Dow Jones Industrial Average, S&P 500, Nasdaq and historical company earnings data from the internet. People who read this book will need a good working knowledge of Microsoft Excel, Excel VBA and Python.
: Ingvar16 , 04:25 | : 0 |
: Exploring Microsoft Office Excel 2019 Comprehensive
: Mary Anne Poatsy
: Pearson
: 2019
: PDF
: 795
: 71 Mb
: English

Now live! SUMMER 2020 DIGITAL UPDATE for digital content
Microsoft Office 365 updates are reflected in the eText and associated MyLab for this title. Instructors, to learn more, contact your Pearson representative.
For courses covering introductory Microsoft Excel.
: vitvikvas , 04:19 | : 0 |
: Federated Learning for Wireless Networks
: Choong Seon Hong, Latif U. Khan, Mingzhe Chen
: Springer
: Wireless Networks
: 2021
: 257
:
: pdf (true)
: 10.6 MB

Recently Machine Learning schemes have attained significant attention as key enablers for next-generation wireless systems. Currently, wireless systems are mostly using Machine Learning schemes that are based on centralizing the training and inference processes by migrating the end-devices data to a third party centralized location. However, these schemes lead to end-devices privacy leakage. To address these issues, one can use a distributed machine learning at network edge. In this context, Federated Learning (FL) is one of most important distributed learning algorithm, allowing devices to train a shared machine learning model while keeping data locally. However, applying FL in wireless networks and optimizing the performance involves a range of research topics. For example, in FL, training machine learning models require communication between wireless devices and edge servers via wireless links. Therefore, wireless impairments such as uncertainties among wireless channel states, interference, and noise significantly affect the performance of FL. On the other hand, federated-reinforcement learning leverages distributed computation power and data to solve complex optimization problems that arise in various use cases, such as interference alignment, resource management, clustering, and network control. Traditionally, FL makes the assumption that edge devices will unconditionally participate in the tasks when invited, which is not practical in reality due to the cost of model training.
: Ingvar16 , 04:07 | : 0 |

: Jaguar The Sports Car (Jaguar Memories Collector's)
: Kelsey Publishing
: 2022
: 100
: English
: PDF
: 51 MB

Jaguar The Sports Car (Jaguar Memories Collector's).
: Gerza , 04:06 | : 0 |

: Ships Monthly 2022-02
: Kelsey Publishing
: True PDF
: 68
: 16 Mb
: English

Ships Monthly
: dimarik , 04:06 | : 0 |

: Scenic Railways of Britain 5: Wales & Borders (Railways of Britain Vol.30)
: Key Publishing
: 2022
: PDF
: 100
: 67 Mb
: English

Scenic Railways of Britain 5: Wales & Borders (Railways of Britain Vol.30)
: dimarik , 03:59 | : 0 |
: .
:
:
: 2022
: PDF
: 349
: 12,2 Mb
:

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: vitvikvas , 03:56 | : 0 |

: Audi Quattro (Classics World German)
: Kelsey Publishing
: 2022
: 100
: English
: PDF
: 57 MB

Audi Quattro (Classics World German).
: Gerza , 03:55 | : 0 |

: Modern Porsche (Collector's Edition)
: Kelsey Publishing
: 2022
: 100
: English
: PDF
: 56 MB

Modern Porsche (Collector's Edition).
: Gerza , 03:45 | : 0 |

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