Telerik_UI_For_WPF_2016_3_1024_Dev开发包，免许可破解版，另外附Telerik Ultimate Collection for .NET 2016 R3 SP1所有开发包地址，包含这一版本的所有开发技术，都是破解版，http://download.csdn.net/detail/feng3030206/9738669
2023-01-31 13:05:28
31B
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2016_第七届_蓝桥杯_省赛_电压测量监控设备！！代码！！
功能要求全部完成
设计一个电压测量监控设备，定时通过串口向 PC 机发送电压值，通过串口接收系统配
置参数并保存到 E2PROM 中
2023-01-15 22:27:36
6.29MB
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To earn the OCA Java SE 8 Programmer I Certification, you have to know your Java inside and out, and to pass the exam you need to understand the test itself. This book cracks open the questions, exercises, and expectations you’ll face on the OCA exam so you’ll be ready and confident on test day.
OCA Java SE 8 Programmer I Certification Guide prepares Java developers for the 1Z0-808 with thorough coverage of Java topics typically found on the exam. Each chapter starts with a list of exam objectives mapped to section numbers, followed by sample questions and exercises that reinforce key concepts. You’ll learn techniques and concepts in multiple ways, including memorable analogies, diagrams, flowcharts, and lots of well-commented code. You’ll also get the scoop on common exam mistakes and ways to avoid traps and pitfalls.
2023-01-10 08:33:56
17.79MB
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应用组合学 Applied Combinatorics 2016 Edition Mitchel T. Keller
2023-01-09 12:29:15
5.24MB
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Yang Gao1, Oscar Beijbom1, Ning Zhang2∗, Trevor Darrell1 †Bilinear models has be
2023-01-07 20:46:27
2.06MB
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The Deep Learning textbook is a resource intended to help students and practitioners enter the field of machine learning in general and deep learning in particular.
Table of Contents
Chapter 1 Introduction
Part I: Applied Math and Machine Learning Basics
Chapter 2 Linear Algebra
Chapter 3 Probability and Information Theory
Chapter 4 Numerical Computation
Chapter 5 Machine Learning Basics
Part II: Modern Practical Deep Networks
Chapter 6 Deep Feedforward Networks
Chapter 7 Regularization
Chapter 8 Optimization for Training Deep Models
Chapter 9 Convolutional Networks
Chapter 10 Sequence Modeling: Recurrent and Recursive Nets
Chapter 11 Practical Methodology
Chapter 12 Applications
Part III: Deep Learning Research
Chapter 13 Linear Factor Models
Chapter 14 Autoencoders
Chapter 15 Representation Learning
Chapter 16 Structured Probabilistic Models for Deep Learning
Chapter 17 Monte Carlo Methods
Chapter 18 Confronting the Partition Function
Chapter 19 Approximate Inference
Chapter 20 Deep Generative Models
2023-01-07 16:10:50
77.75MB
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