生命之旅

Super Intelligence

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生命不限于个体。并非所有生命拥有意识，但所有生命都拥有智能。这些智能体通过大量并行和多层迭代的方式形成新的智能体。细胞、器官、个体、国家、地球，不论从哪个层级上观察，都是一个“智能体”。
人类作为智能的一环，需跳出自身层级，用超出人类自身感知、情感和意识的方式去理解生命。

关于本书

该书最终的目的是：通过理解智能，学习如何学习。

如何机器学习
如何大脑学习

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A Visual and Interactive Guide to the Basics of Neural Networks

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J Alammar

Motivation

I’m not a machine learning expert. I’m a software engineer by training and I’ve had little interaction with AI. I had always wanted to delve deeper into machine learning, but never really found my “in”. That’s why when Google open sourced TensorFlow in November 2015, I got super excited and knew it was time to jump in and start the learning journey. Not to sound dramatic, but to me, it actually felt kind of like Prometheus handing down fire to mankind from the Mount Olympus of machine learning. In the back of my head was the idea that the entire field of Big Data and technologies like Hadoop were vastly accelerated when Google researchers released their Map Reduce paper. This time it’s not a paper – it’s the actual software they use internally after years and years of evolution.

So I started learning what I can about the basics of the topic, and saw the need for gentler resources for people with no experience in the field. This is my attempt at that.

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Supercharging Android Apps with TensorFlow

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J Alammar

Supercharging Android Apps With TensorFlow (Google’s Open Source Machine Learning Library)
In November 2015, Google announced and open sourced TensorFlow, its latest and greatest machine learning library. This is a big deal for three reasons:

Machine Learning expertise: Google is a dominant force in machine learning. Its prominence in search owes a lot to the strides it achieved in machine learning.
Scalability: the announcement noted that TensorFlow was initially designed for internal use and that it’s already in production for some live product features.
Ability to run on Mobile.

This last reason is the operating reason for this post since we’ll be focusing on Android. If you examine the tensorflow repo on GitHub, you’ll find a little tensorflow/examples/android directory. I’ll try to shed some light on the Android TensorFlow example and some of the things going on under the hood.

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TensorFlow Android Camera Demo

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作者：tensorflow

This folder contains an example application utilizing TensorFlow for Android devices.

Description

The demos in this folder are designed to give straightforward samples of using TensorFlow in mobile applications.

Inference is done using the TensorFlow Android Inference Interface, which may be built separately if you want a standalone library to drop into your existing application.

A device running Android 5.0 (API 21) or higher is required to run the demo.

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TensorFlow Android Stand-Alone Demo

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Author：miyosuda

Android demo source files extracted from original TensorFlow source. (TensorFlow r0.10)

To build this demo, you don’t need to prepare build environment with Bazel, and it only requires AndroidStudio.

If you would like to build jni codes, only NDK is requied to build it.

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Applications of TensorFlow

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作者：AI研习社

通过一些TensorFlow实际应用，让大家对TensorFlow有理性和感性的双层认知。

随着谷歌2015年发布开源人工系统TensorFlow，让本就如火如荼的深度学习再添一把火，截至现在，TensorFlow已经历了多个版本演进，功能不断完善，AI开发者也能灵活自如的运用TensorFlow解决一些实际问题，下面雷锋网(公众号：雷锋网)会对一些比较实用的TensorFlow应用做相关整理，让大家对TensorFlow有理性和感性的双层认知。

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Compare of AI Platform

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作者：AI研习社

TensorFlow 与其他平台、框架对比，具有哪些优点及劣势？

作为机器学习领域、尤其是 Python 生态圈最受欢迎的框架平台，TensorFlow 具有许多吸引开发者的优点。其中最显而易见的是谷歌的技术支持和完善的社区（庞大用户群）。这些都为 TensorFlow 的普及打下了基础。但是，开发者需要了解 Tensorflow 在技术上有哪些值得一提的优势，又有哪些不足，以便在处理特定任务时进行工具选择。而这些，必须要在与其他平台、框架的对比中才能凸显。顺便说一句老生常谈的话，没有万能的工具，只有在不同应用场景下最合适的选择。 因此，雷锋网(公众号：雷锋网)整理了介绍 Tensorflow、Caffe、Microsoft Cognitive Toolkit （CNTK）、MXnet、Torch 等平台框架，以及对它们做横向对比的文章，供读者按图索骥。

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