谷歌工智能开源项目Tensorflow预示着硬件领域的重大变革

摘要：在谷歌内部，处理图像识别、语音识别和语言翻译等任务时，TensorFlow依赖于配备图像处理单元（GPU）的机器，和被用于渲染游戏图像的芯片等，但对其它的任务也擅长。它对这些芯片的依赖比想象中的更多。

作者：litai wong/wangaicc

我想很多程序员应该记得 GitHub 上有一个 Awesome - XXX 系列的资源整理。awesome-python 是 vinta 发起维护的 Python 资源列表，内容包括：Web框架、网络爬虫、网络内容提取、模板引擎、数据库、数据可视化、图片处理、文本处理、自然语言处理、机器学习、日志、代码分析等。由伯乐在线持续更新。

Awesome 系列虽然挺全，但基本只对收录的资源做了极为简要的介绍，如果有更详细的中文介绍，对相应开发者的帮助会更大。这也是我们发起这个开源项目的初衷。

作者：yjango

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。

1. 如何机器学习

2. 如何大脑学习

   Read more »

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.

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:

1. 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.
2. Scalability: the announcement noted that TensorFlow was initially designed for internal use and that it’s already in production for some live product features.
3. 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.

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.