Posted Mar 31, 2015 by Sebastian McKenzie
In the past few months Babel has been welcomed into several major communities such as Node, React, Ember, Backbone, Angular, Rails, and many others. We launched the Users page only a few weeks ago and it's really cool to see everyone that is using it. Companies like CloudFlare, Netflix, Mozilla, and Yahoo!. Projects like Ghost, Atom, Mapbox, and so many more.
We've seen tons of blog posts, talks, events, courses all about ES6+ using Babel, and official Babel tools have been downloaded nearly 2 million times.
Today we are making by far the largest release of Babel ever.
If you're upgrading from Babel 4.x please see the breaking changes.
This release includes the new ES7 proposals:
The entire internal traversal and transformation pipeline has undergone a rewrite that substantially increases flexibility and will allow many future pipeline performance optimisations.
This release also brings a plugin API, this allows consumers to plug in their own custom transformers to utilise the powerful transformation mechanisms that Babel has to offer.
You can view the complete CHANGELOG here.
And as usual if you run into any regressions please report them immediately.
TC39 Process
In this release you'll start to see us aligned with the TC39 process. The TC39 is the technical committee from ECMA that writes the ECMAScript standard. Their process is categorised into 5 stages:
- Stage 0 - Strawman
- Stage 1 - Proposal
- Stage 2 - Draft
- Stage 3 - Candidate
- Stage 4 - Finished
Proposals that are stage 2 or above are enabled in Babel by default. Now this does not mean that they're guaranteed to be included in future ECMAScript specifications or even Babel itself. Stage 2 is considered a good point for inclusion by default in Babel due to their relative maturity and need for critical proposal feedback.
Now let's dive into the changes we made to 5.0.
Contents:
New Features
New Proposals
Stage 0: Class Properties
Jeff Morrison's stage 0 class property initializers proposal fills the void of property composition on classes. These are analogous with the class properties example listed in the React 0.13 beta announcement.
Example:
class Person {
firstName = "Sebastian";
static lastName = "McKenzie";
}
assert(new Person().firstName, "Sebastian");
assert(Person.lastName, "McKenzie");
Usage:
require("babel").transform("code", {
optional: ["es7.classProperties"]
});
// or
require("babel").transform("code", { stage: 0 });
$ babel --optional es7.classProperties script.js
# or
$ babel --stage 0 script.js
Stage 1: Decorators
Yehuda Katz' stage 1 decorators proposal allows you to elegantly compose property descriptors and metadata decoration. In the future this will allow the powerful Ember Object Model to easily be represented with native classes.
Example:
function concat(...args) {
let sep = args.pop();
return function(target, key, descriptor) {
descriptor.initializer = function() {
return args.map(arg => this[arg]).join(sep);
}
}
}
function autobind(target, key, descriptor) {
var fn = descriptor.value;
delete descriptor.value;
delete descriptor.writable;
descriptor.get = function () {
var bound = fn.bind(this);
Object.defineProperty(this, key, {
configurable: true,
writable: true,
value: bound
});
return bound;
};
}
class Person {
firstName = "Sebastian";
lastName = "McKenzie";
@concat("firstName", "lastName", " ") fullName;
@concat("lastName", "firstName", ", ") formalName;
@autobind
getFullName() {
return `${this.firstName} ${this.lastName}`;
}
}
assert(new Person().fullName, "Sebastian McKenzie");
assert(new Person().formalName, "McKenzie, Sebastian");
assert(new Person().getFullName.call(null), "Sebastian McKenzie");
Usage:
require("babel").transform("code", {
optional: ["es7.decorators"]
});
// or
require("babel").transform("code", { stage: 1 });
$ babel --optional es7.decorators script.js
# or
$ babel --stage 1 script.js
Stage 1: Export Extensions
Lee Byron's stage 1 additional export-from statements proposal completes the symmetry between import and export statement, allowing you to easily export namespaces and defaults from external modules without modifying the local scope.
Exporting a default
export foo from "bar";
equivalent to:
import _foo from "bar";
export { _foo as foo };
Exporting a namespace
export * as ns from "mod";
equivalent to:
import * as _ns from "mod";
export { _ns as ns };
Usage:
require("babel").transform("code", {
optional: ["es7.exportExtensions"]
});
// or
require("babel").transform("code", { stage: 1 });
$ babel --optional es7.exportExtensions script.js
# or
$ babel --stage 1 script.js
React Optimisations
In preparation for React 0.14, Babel supports some optimisation transformers for JSX.
Constant Elements
Starting with 0.14 ReactElements and their props objects can be treated as as value types. i.e. any instance is conceptually equivalent if all their values are the same.
Take this function for example:
import React from "react";
function render() {
return
;
}
This can be optimized by moving the JSX out of the function body so that each time it is called the same instance is returned:
import React from "react";
var _ref =
;
function render() {
return _ref;
}
Not only does it allow us to reuse the same objects, React will automatically
bail out any reconciliation of constant components - without a manual
shouldComponentUpdate
.
Usage:
require("babel").transform("code", {
optional: ["optimisation.react.constantElements"]
});
$ babel --optional optimisation.react.constantElements script.js
Inline Elements
Production only
Inline Elements should only be enabled in production as multiple React warning messages are suppressed which is extremely risky in development.
Starting with React 0.14 ReactElements can be inlined:

{bar}<Baz key="baz" />

as objects:
{ type: 'div', props: { className: 'foo', children:
[ bar, { type: Baz, props: { }, key: 'baz', ref: null } ]
}, key: null, ref: null }
This improves performance over the existing React.createElement
call by
inlining the result of it.
Usage:
require("babel").transform("code", {
optional: ["optimisation.react.inlineElements"]
});
$ babel --optional optimisation.react.inlineElements script.js
.babelrc
Babel 5.0.0 has support for .babelrc
out of the box across its entire
range of integrations. This means that it will work across
babel/register
,
babel-node
as well as across the entire range
of build system plugins and module loaders such as
babel-loader
,
babelify
, and others.
.babelrc
is equivalent to JSHint's .jshintrc
and
JSCS' .jscsrc
.
{
"stage": 1,
"ignore": [
"foo.js",
"bar/**/*.js"
]
}
See the docs for more info.
Plugin API
5.0.0 also introduces the long anticipated plugin API. This allows you to hook into the powerful traversal and transformation internals of Babel. See the docs for more info.
Breaking Changes
Experimental Option
The experimental
option has been removed. Fear not though, there is a
replacement. Babel now categories the ES7 transformers by
TC39 stages.
tl;dr If you're using the experimental
option, simply change it to
$ babel --stage 0
or { stage: 0 }
.
Reminder: Proposals that are stage 2 or above are enabled by default.
Stage 0
es7.classProperties
es7.comprehensions
Stage 1
es7.asyncFunctions
es7.decorators
es7.exportExtensions
es7.objectRestSpread
Stage 2 (Stage 2 and above are enabled by default)
es7.exponentiationOperator
For a list of all current ES7 proposals please see the tc39/ecma262 repo.
returnUsedHelpers
option
The returnUsedHelpers
option has been renamed to metadataUsedHelpers
and the returning result
object has been changed from usedHelpers
to metadata.usedHelpers
.
Class Changes
5.0.0 introduces some updated derived class semantics that are long overdue.
super()
must be called in a derived class constructor.
class Foo extends Bar {
constructor() {
// no `super();`
}
}
Access to this
before super()
in a derived class constructor is not
allowed.
class Foo extends Bar {
constructor() {
this.foo; // `this` access before `super();`
super();
}
}
super()
is only allowed in derived class constructors.
class Foo {
constructor() {
super(); // not in a derived constructor
}
}
Removed Features
- The playground has been removed so development can be focussed on mainstream ES features and proposals. This also reduces the risk of syntactic conflicts preventing certain official features from being implemented.
- Abstract references have been removed as the proposal has been superseded. Support for one or more of the superseding proposals may be implemented in the future.
In closing, we hope that you are now as excited about this release as we are. There's a lot that went into it, and we believe this will set us up for a long time into the future.
— The Babel team
Imports are now hoisted
In 4.x
, imports were inlined as where they appeared in the code. Which means that this code:
global.test = 'test'
import './test'
would compile to:
'use strict';
global.test = 'test';
require('./test');
However, from 5.x
on, this behaviour has been changed in order to comply with the ES6 spec and imports will now be hoisted. What this means in practical code is that the snippet above will get converted to something like:
'use strict';
require('./test');
global.test = 'test';
If your code required certain bits and pieces to be executed in between a specific module being imported -which might be the case while testing code and you need to fake some window
properties :)- you may want to extract that away into its own file and import it before the code that needs it.


| Patient | Joe Schmoe | Language | English |
| Date of birth | April 20, 1969 | Sex | Male |
| Race | UNK (UNK) |
Ethnicity | |
| Contact info | Primary Home: Van by the River Kennesaw, GA 30144, US |
Patient IDs | 5973687 2.16.840.1.113883.19.1 5973687 2.16.840.1.113883.3.5416.1.1577 |
| Document Id | CCDA 2.16.840.1.113883.19.1 |
| Document Created: | April 4, 2009, 06:37:13 |
| Referring or Transitioning Providers Name |
| Author | Reza Parizi, PhD |
| Contact info | 1100 S Marietta Drive Marietta, GA 30600, US Tel (Work Place): tel:+1(555)445-3335 |
| Encounter Id | 1 3572a0d5-3fd8-82e8-4f08-d0c490fcc493 | ||
| Encounter Date | From April 3, 2009, 13:40:29 to April 3, 2009, 23:59:59 | ||
| Encounter Location | KSU Health Center | ||
| Responsible party | Reza Parizi, PhD | ||
| Contact info | 1100 S Marietta Drive Marietta, GA 30600, US Tel (Work Place): tel:+1(555)445-3335 |
| Document maintained by | KSU Health Center |
| Contact info | 1100 S Marietta Drive Marietta, GA 30600, US Tel (Work Place): tel:+1(555)445-3335 |
No Procedure information.
No information.
No Vital Signs information.
No Social History information.
No Family History information.
No information.
No information.
No information.
No information.
No information.
No information.
| Reason for visit | Date |
|---|---|
| 4/3/09 Pls send meds. | 04/03/2009 |
No information.
No information.
No information.
No information.
| Payer Id | Policy Id |
|---|---|
| 9845 | 6098 |
No information.


public interface MetricNames
| 限定符和类型 | 字段和说明 |
|---|---|
static String |
LOCAL_REQUESTS
Counter of requests handled locally
|
static String |
MEMORY_FREE_PERCENT
PercentGauge of memory free
|
static String |
MESSAGES_SENT
Counter of messages sent in superstep
|
static String |
PERCENT_LOCAL_REQUESTS
PercentGauge of requests that are handled locally
|
static String |
RECEIVED_BYTES
Number of bytes received in superstep
|
static String |
REMOTE_REQUESTS
Counter of requets sent on the wire
|
static String |
SEND_AGGREGATORS_TO_MASTER_REQUESTS
Counter for sending aggregated values from worker owner to master
|
static String |
SEND_AGGREGATORS_TO_OWNER_REQUESTS
Counter for sending aggregators from master to worker owners
|
static String |
SEND_AGGREGATORS_TO_WORKER_REQUESTS
Counter for sending aggregators from worker owner to other workers
|
static String |
SEND_PARTITION_CURRENT_MESSAGES_REQUESTS
Counter for sending a partition of messages for current superstep
(used during partition exchange)
|
static String |
SEND_PARTITION_MUTATIONS_REQUESTS
Counter for sending a partition of mutations
|
static String |
SEND_VERTEX_REQUESTS
Counter for sending vertices requests
|
static String |
SEND_WORKER_AGGREGATORS_REQUESTS
Counter for sending aggregated values from one worker's vertices
|
static String |
SEND_WORKER_MESSAGES_REQUESTS
Counter for sending a partition of messages for next superstep
|
static String |
SENT_BYTES
Number of bytes sent in superstep
|
static String |
TOTAL_REQUESTS
Guage of total requests
|
static String |
VERTICES_IN_MUTATION_REQUEST
Histogram for vertices in mutations requests
|
static final String PERCENT_LOCAL_REQUESTS
static final String SEND_VERTEX_REQUESTS
static final String SEND_WORKER_MESSAGES_REQUESTS
static final String SEND_PARTITION_CURRENT_MESSAGES_REQUESTS
static final String SEND_PARTITION_MUTATIONS_REQUESTS
static final String SEND_WORKER_AGGREGATORS_REQUESTS
static final String SEND_AGGREGATORS_TO_MASTER_REQUESTS
static final String SEND_AGGREGATORS_TO_OWNER_REQUESTS
static final String SEND_AGGREGATORS_TO_WORKER_REQUESTS
static final String VERTICES_IN_MUTATION_REQUEST
Copyright © 2011-2014 The Apache Software Foundation. All Rights Reserved.


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The FME Graph is an app capable of showing you information about the graph. When using this app, the information you provide is interpreted using Dialogflow, which has its own privacy policy.
When you invoke our app or ask us something, your request will be directed to Dialogflow. Then, if Dialogflow detects that your request needs information about the graph to fulfill it, it will make a request to our server, which will access the graph, generate a response and send it to Dialogflow, which will send it back to you. If Dialogflow doesn't need information about the graph, it will directly respond to you.
When asking a question or invoking our app, we collect the information that the Actions by Google API provides us in order to fulfill your request, as mentioned in the previous section. After we fulfill the request, this is the only information that we retain in our servers:
We use the information that the Actions by Google API provides us to respond back to you. Also, we use the history of queries saved at Dialogflow for the sole purpose of improving the query recognition of the app. Finally, the logs saved in our server are only used to detect potential abuse of our server and attacks such as DoS or DDoS.
We do not share our logs nor the information provided by Dialogflow to us with anyone else. As for the data collected by Dialogflow, check their privacy policy to learn more.
You can contact us here:


{% extends "layout.html" %} {% block page_title %} Money Claims Prototype {% endblock %} {% block content %} BETA This is a new service – your feedback will help us to improve it. Back Create an account Create an account Enter your email address Email address Create password Must be at least 8 characters, including: at least 1 letter at least 1 number or symbol - such as &, £ or * Enter password Confirm password Create account {% endblock %}
BETA This is a new service – your feedback will help us to improve it.


Please consider reviewing all information made available by the Australia Federal Government with regards to COVID-19 (Coronavirus.)
| City | Answer | Source |
|---|---|---|
| Brisbane | No (with conditions) | Queensland State Government |
| Sydney | No (with conditions) | NSW State Government |
| Melbourne | No (with conditions) | Victoria State Government |
| Perth | No (with conditions) | WA State Government |
| Adelaide | No (with conditions) | SA State Government |
| Darwin | No (with conditions) | NT State Government |
| Hobart | No (with conditions) | Tasmania State Government |
| Canberra | No (with conditions) | ACT Government |
We have taken all possible care in preparing this material, using sources that we believe are reliable and accurate at the time of publication, be we do not give a warranty in respect of the information provided and note that the information should not be relied upon without confirming the above ‘answers’ via the link in the ‘source’ column are accurate, complete and up-to-date. No one associated with this website can be held liable if you fail to comply with relevant legislation or directives.
Nothing on this website constitutes medical or legal advice. Speak to a medical professional if you are feeling unwell; contact a legal practitioner if you need information or advice on your legal obligations.


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[1]:
%run ../initscript.py
HTML("""

Enter Password:

<input id="password" type="password"/>
<button onclick="done()" style="border-radius: 12px;">Submit</button>

<button onclick="unlock()" style="border-radius: 12px;">Unclock</button>
show code
""")
[1]:
[2]:
%run loadmlfuncs.py
Current Status of Deep Learning¶
Achievements¶
Deep learning has achieved the following breakthroughs, all in historically difficult areas of machine learning:
Near-human-level image classification
Near-human-level speech recognition
Near-human-level handwriting transcription
Improved machine translation
Improved text-to-speech conversion
Digital assistants such as Google Now and Amazon Alexa
Near-human-level autonomous driving
Improved ad targeting, as used by Google, Baidu, and Bing
Improved search results on the web
Ability to answer natural-language questions
Superhuman Go playing
Hardware¶
Although our laptop can run small deep-learning models, typical deep-learning models used in computer vision or speech recognition require orders of magnitude more computational power.
Throughout the 2000s, companies like NVIDIA and AMD have been investing billions of dollars in developing fast, massively parallel chips, graphical processing units (GPUs), to power the graphics of increasingly photorealistic video games — cheap, single-purpose supercomputers designed to render complex 3D scenes on the screen in real time.
At the end of 2015, the NVIDIA TITAN X, a gaming GPU that cost $1,000 can perform 6.6 trillion float32
operations per second. That is about 350 times more than what you can get out of a modern laptop. Meanwhile, large companies train deep-learning models on clusters of hundreds of GPUs of a type developed specifically for the needs of deep learning, such as the NVIDIA Tesla K80. The sheer computational power of such clusters is something that would never have been possible without modern
GPUs.
The deep-learning industry is starting to go beyond GPUs and is investing in increasingly specialized, efficient chips for deep learning. In 2016, at its annual I/O convention, Google revealed its tensor processing unit (TPU) project: a new chip design developed from the ground up to run deep neural networks, which is reportedly 10 times faster and far more energy efficient than top-of-the-line GPUs.
If you don’t already have a GPU that you can use for deep learning, then running deep-learning experiments in the cloud is a simple, low cost way for you to get started without having to buy any additional hardware. But if you’re a heavy user of deep learning, this setup isn’t sustainable in the long term or even for more than a few weeks.
Investment¶
As deep learning became the new state of the art for computer vision and eventually for all perceptual tasks, industry leaders took note. What followed was a gradual wave of industry investment far beyond anything previously seen in the history of AI.
In 2011 (right before deep learning took the spotlight), the total venture capital investment in AI was around $19 million
By 2014, the total venture capital investment in AI had risen to $394 million
Google acquired the deep-learning startup DeepMind for a reported $500 million — the largest acquisition of an AI company in history.
Baidu started a deep-learning research center in Silicon Valley, investing $300 million in the project.
Intel acquired a deep-learning hardware startup Nervana Systems for over $400 million.
There are currently no signs that this uptrend will slow any time soon.
Cases¶
As entrepreneurs of AI start-ups, Alice and Bob had received similar amount of investments and competed in the same market
Alice spent lots of money to hire top engineers in AI field
Bob hired only mediocre engineers and spent most of his money to obtain high quality data with larger size
Who will you invest? Why?
[3]:
hide_answer()
[3]:
acc1 = net_compare(512, .25)
acc2 = net_compare(128, 1)
print('The accuracy of a complicated model (with 512 nodes) with less (one fourth of) training data:', acc1)
print('The accuracy of a simple model (with 128 nodes) and full training data:', acc2)
print('The improvement is {}%!'.format(round((acc2-acc1)/(1-acc1)*100,2)))
Suppose you’re trying to develop a model that can take as input images of a clock
and can output the time of day. What machine learning approach will you use?
[4]:
hide_answer()
[4]:
If you choose to use the raw pixels of the image as input data, then you have a difficult machine-learning problem on your hands. You’ll need a convolutional neural network to solve it, and you’ll have to expend quite a bit of computational resources to train the network.
But if you already understand the problem at a high level, you can write a five-line Python script to follow the black pixels of the clock hands and output the \((x, y)\) coordinates of the tip of each hand. Then a simple machine-learning algorithm can learn to associate these coordinates with the appropriate time of day. For example, the long hand has \((x=0.7, y=0.7)\) and the short hand has \((x=0.5, y=0.0)\) in the first image, and the long hand has \((x=0.0, y=1.0)\) and the short hand has \((x=-0.38, y=0.32)\) in the second image.
You can go even further: do a coordinate change, and express the \((x, y)\) coordinates as the angle of each clock hand. For example, the long hand has angle \(45\) degree and the short hand has angle \(0\) degree in the first image, and the long hand has angle \(90\) degree and the short hand has angle \(140\) degree in the second image. At this point, your features are making the problem so easy that no machine learning is required; a simple rounding operation and dictionary lookup are enough to recover the approximate time of day.


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Hello!!!! :)
Hey, how are you doing?
Have you guys seen "The Nun"?
Yes, it was quite spooky.
Im fine, how about you?