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转载 Ubuntu16.04 Caffe 安装步骤记录（超详尽）

版权声明：转载请说明出处：http://blog.csdn.net/yhaolpz https://blog.csdn.net/yhaolpz/article/details/71375762目录(?)[+]历时一周终于在 ubuntu16.04 系统成功安装 caffe 并编译，网上有很多教程，但是某些步骤并没有讲解详尽，导致配置过程总是出现各种各样匪夷所思的问题，尤其对于新手而言更是欲哭无泪，...

2018-03-31 12:45:15 934

原创 Ubuntu ARM64 安装 Tensorflow

Compiling Bazel from Source (bootstrapping)You can build Bazel from source without using an existing Bazel binary by doing the following:Ensure that JDK 8, Python, Bash, zip, and the usual C++ build t...

2018-03-30 15:36:26 3693 3

InstallSQLiteStudio-3.2.1.7z
SQLiteStudio是一款绿色小巧的SQLite数据库可视化工具，支持多国语言，能导出多种数据格式，如：csv、html、plain、sql、xml等。除此之外，软件功能强大，能同时打开多个数据库文件，也支持查看和编辑二进制字段，从而实现有效的数据库编辑管理操作。

2020-04-17

Introduction to Deep Learning.pdf
Introduction to Deep Learning Introduction to Deep Learning

2019-06-16

Deep Learning for Search.pdf
Deep Learning for Search is a practical book about how to use (deep) neural networks to help build effective search engines. This book examines several components of a search engine, providing insights on how they work and guidance on how neural networks can be used in each context. Emphasis is give

2019-06-16

PyTorch Recipes_ A Problem-Solution Approach.pdf
PyTorch Recipes _ A Problem-Solution Approach This book is intended for data scientists, natural language processing engineers, artificial intelligence solution developers, existing practitioners working on graph computation frameworks, and researchers of graph theory. This book will get you started with understanding tensor basics, computation, performing arithmetic-based operations, matrix algebra, and statistical distribution-based operations using the PyTorch framework.

2019-06-15

Deep Learning and Missing Data in Engineering Systems.pdf
Deep Learning and Missing Data in Engineering Systems

2019-06-15

Deep Learning for Dummies.pdf
Deep Learning for Dummies When you work through Deep Learning For Dummies, you gain access to a lot of example code that will run on a standard Mac, Linux, or Windows system. You can also run the code online using something like Google Colab. (We provide pointers on how to get the information you need to do this.) Special equipment, such as a GPU, will make the examples run faster. However, the point of this book is that you can create deep learning code no matter what sort of machine you have as long as you’re willing to wait for some of it to complete. (We tell you which examples take a long time to run.)

2019-06-15

Deep Reinforcement Learning for Wireless Networks.pdf
Deep Reinforcement Learning for Wireless Networks

2019-06-15

Fog Computing, Deep Learning and Big Data Analytics-Research Directions.pdf
Fog Computing, Deep Learning and Big Data Analytics-Research Directions

2019-06-15

Deep Learning_ Convergence to Big Data Analytics.pdf
Deep Learning_ Convergence to Big Data Analytics

2019-06-15

Deep Learning_ Fundamentals, Theory and Applications.pdf
Deep Learning_ Fundamentals, Theory and Applications

2019-06-14

Big Data Analysis and Deep Learning Applications
Big Data Analysis and Deep Learning Applications Proceedings of the First International Conference on Big Data Analysis and Deep Learning

2019-06-14

Advances in Deep Learning.pdf
Advances in Deep Learning The books of the series refer to the analysis and understanding of large, complex, and/or distributed data sets generated from recent digital sources coming from sensors or other physical instruments as well as simulations, crowd sourcing, social networks or other internet transactions, such as emails or video click streams and other.

2019-06-14

Learn Keras for Deep Neural Networks
Learn Keras for Deep Neural Networks深度学习书，使用keras

2019-06-14

Practical Computer Vision Applications Using Deep Learning with CNNs
Practical Computer Vision Applications Using Deep Learning with CNNs

2019-06-14

Natural Language Processing Recipes: Unlocking Text Data with
Natural Language Processing Recipes: Unlocking Text Data with Machine Learning and Deep Learning using Python

2019-06-12

动手学深度学习.pdf
动手学深度学习，本书将全面介绍深度学习从模型构造到模型训练的方方面面，以及它们在计算机视觉和自然语言处理中的应用。我们不仅将阐述算法原理，还将基于Apache MXNet对算法进行实现，并实际运行它们。本书的每一节都是一个Jupyter记事本。它将文字、公式、图像、代码和运行结果结合在了一起。读者不但能直接阅读它们，而且可以运行它们以获得交互式的学习体验。

2019-06-12

Deep Learning for Computer Vision with Python 3 ImageNetBundle.pdf
Deep Learning for Computer Vision with Python 3 ImageNetBundle

2019-06-10

Deep Learning for Computer Vision with Python 2 Practitioner Bundle.pdf
Deep Learning for Computer Vision with Python 2 Practitioner Bundle.pdf

2019-06-10

Deep Learning for Computer Vision with Python 1 Starter Bundle.pdf
Deep Learning for Computer Vision with Python 1 Starter Bundle

2019-06-10

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