系統設計者的話：學英文根本不用背，你講中文，你有在背中文嗎？學英文也是一樣的道理，那是自然而然就會的東西，用背的方式最後肯定都是不行的。 更多...

最好的學習就是有英文的父母，不斷的跟你說話、耐心的教導你，時間長了，你也就慢慢就融入其中了。

但是我們沒有英文父母怎麼辦？看影片學英文就學的起來嗎？學不起來。

我們從小到大，也看過了不少英文電影，至少也看過1、200部也跑不掉，但是為什麼看了這麼多的英文電影，英文還是很爛呢？

主要的原因是我們沒有從基礎性的強化學習。舉個例子，西方的幼兒出生時，他至少聽了各種大人講英文至少聽了2年，才慢慢開始說英文、與大人互動。

而且都是一字一句慢慢的說、慢慢的教、漸進式的、慢慢的、越來越習慣、越來越聽的懂、越來越會說的過程。

那麼我們看的影片、電影過程中，都是大人成熟型的講話速度、方式、語調，我們怎麼可能跟的上？

一旦跟不上，思想中就會產生覺的好難哦、我不行種種念頭，那麼產生了這種潛在式的排斥心理作用，那麼你就更難學的會了。


所以我們必須打好基礎、深厚的基礎，萬丈高樓平地起，唯有打下深厚的基礎，後面你走的路才會越來越順。

程式界裏有句話，程式債欠的越多，最後就只能放棄。學英文也是一樣，英文單字債欠的越多，你就直接上來文法、句型、語調，最後也只能放棄。

基於上述的思維，我打造了這套系統，從最基礎的單字學習起，不斷的強化、加深你對這個單字的聽力，透過各式各樣的影片、各式各樣的人、各種不同的場景。

就只是為了訓練一個單字的聽力，無數的人跟你講同一個單字，講個20遍、50篇、100遍(且不重覆)，我就不信學不起來。


這套系統做出來後，我自己在使用的過程中，確實真的能聽出來他們講的這個單字，而且他們講的速度再快也能聽的出來。

能聽的出來，就知道他講的大概意思；能夠知道他講的大概意思，就能夠用幾個簡單的單字回應；能夠回應，也就能夠對話；當能夠對話，就能夠再修正文法、句型、語調。


我的目地就是希望自己至少能聽的懂人家在說什麼，用英文跟人家對話，大家共同努力、學習。若有其它想法或對這套系統設計的建議，也可以與我聯絡：ichich2013@gmail.com

TensorFlow for JavaScript (TensorFlow @ O’Reilly AI Conference, San Francisco '18)
觀看次數：419
about
tensorflow
and
javascript
today
my
ping
and
we
work
on
tensor
flow
gs
here
happens
in
python
right
that's
that's
activation
functions
and
this
was
a
do
ml
in
the
browser
there's
a
lot
of
is
about
400
lines
of
javascript
code
it
and
aligned
it
with
what
we're
doing
tensorflow
internally
with
eager
launched
tensorflow
gs
last
April
this
is
all
done
in
the
browser
there's
you're
training
in
the
webcam
there's
with
tensorflow
gs
to
avoid
it's
kind
of
a
funny
animation
but
there's
a
little
all
through
using
tensorflow
gs
and
continuing
to
play
this
one's
really
magenta
jas
which
is
built
on
tensorflow
synthesizer
that
learns
how
the
plate
open-source
it's
called
face
api
gs
so
components
to
tensorflow
jas
there's
a
is
all
powered
in
the
browser
by
webgl
aspect
for
the
browsers
we
bootstrap
all
tensorflow
python
so
it's
you
get
the
high-end
cpu
gpu
and
then
eventually
anyone
who's
worked
with
tensorflow
save
models
directly
in
the
browser
there's
a
browser
you
can
import
pre
train
models
tensorflow
for
poets
it's
a
very
similar
we're
gonna
import
at
tensorflow
/tf
gs
X
equals
ax
squared
plus
B
to
the
power
we've
declared
somewhere
in
this
code
left-right
a
sequential
model
this
is
very
charis
different
filters
and
kernel
size
raivo
activation
functions
again
this
is
we
flatten
out
our
layers
browser
and
on
uhnot
gs2
file
and
api's
we
use
the
higher-level
core
and
higher-level
layers
API
on
the
lower
powerdirector
browser
for
this
one
i'll
versions
of
the
emojis
we
use
every
day
introducing
emoji
scavenger
hunt
emoji
scavenger
hunt
uses
tensorflow
j/s
open-source
meats
machine
learning
meats
javascript
meats
funds
it
works
like
advance
the
next
emoji
while
you're
tensorflow
and
kernel
II
in
mobile
net
import
that
into
our
javascript
app
-
tensorflow
gis
ships
over
python
it's
a
pip
install
tensorflow
j/s
and
you
can
use
the
tensorflow
j's
underscore
converter
for
interacting
with
a
save
charis
style
converter
as
well
for
hdf5
load
those
artifacts
into
the
browser
so
creates
there's
a
weights
link
and
then
a
link
to
the
json
file
which
describes
avoids
some
of
the
caching
that
we
do
we
is
sharding
of
weights
in
the
four
megabyte
chunks
which
cached
nicely
with
modern
browsers
so
it's
only
a
one-time
of
the
today's
tensorflow
ops
and
that
convergence
step
and
we're
always
adding
supported
with
this
compression
step
I
this
is
a
newer
demo
that
we've
we've
poe's
net
which
is
a
human
estimation
postnet
is
not
an
example
of
converting
usbc
in
a
browser
this
is
the
retraining
the
showcase
again
we're
using
mobile
net
the
browser
and
we're
going
to
Train
ping
space
to
play
pac-man
so
he's
gonna
start
collecting
samples
from
the
webcam
moving
his
face
around
he's
collecting
into
that
retraining
step
so
there's
a
collecting
more
and
more
frames
he's
he's
collected
is
frames
he's
gonna
in
the
browser
we
retrained
it
to
those
for
the
our
browser
runtime
that
webgl
powered
runtime
so
this
is
some
benchmarks
we've
done
using
python
and
is
a
high-end
workstation
with
a
sheet
1080
gtx
the
high-end
nvidia
card
so
macbook
with
a
non
integrated
graphics
graphics
card
not
a
standalone
graphics
there's
no
it's
just
the
default
avx
does
the
tensorflow
j
estimate
benchmark
beefy
1080
card
we're
really
close
about
11
milliseconds
per
inference
time
the
100
milliseconds
for
inference
time
but
which
is
we're
just
tensorflow
gs
and
under
no
gs
the
ecosystem
for
no
packages
is
really
awesome
there's
tons
runtime
for
no
gs
v8
super
fast
it's
had
like
Google
and
we've
seen
benchmarks
times
faster
than
the
python
by
enabling
nodejs
and
tensorflow
Express
app
if
anyone's
used
it
it's
defined
and
we're
going
to
do
some
tensorflow
gs
but
note
it's
one
line
of
bring
all
these
components
of
npm
and
tensorflow
gs
and
no
gs
together
we
pitches
that
players
throat
games
so
there's
there's
all
in
tensorflow
gs
that
trains
against
too
hung
up
on
the
intricacies
of
bread-and-butter
ml
problem
of
taking
browser
inside
a
browser
which
trying
to
you
know
the
curveball
has
about
90%
so
just
highlighting
exactly
what's
javascript
playing
html
and
all
the
the
examples
we've
showed
you
they
are
highlighted
where
the
webgl
runtime
kind
of
stacked
up
with
that
python
benchmark
benchmark
against
the
node
gs
runtime
so
I
highlighted
the
node
runtime
itself
is
just
as
fast
as
the
python
runtime
for
uses
and
there's
there's
not
much
code
high-end
code
so
okay
i've
highlighted
a
have
some
high-level
bets
that
we're
it's
our
visualization
library
so
it's
and
do
quick
visualizations
of
your
it'll
be
browser
and
node
specific
so
highly
optimized
data
pipelines
for
of
our
runtimes
benchmarks
I
should
with
webgl
a
lot
of
them
are
the
bottlenecks
or
limitations
for
a
webgl
so
we
use
2d
textures
that
there's
some
bottlenecks
for
downloading
those
textures
reusing
those
textures
so
we're
working
on
webgl
optimization
browser
the
webgl
isn't
the
best
use
offerings
from
chrome
and
Internet
Explorer
and
the
browser
vendors
for
integrations
a
thing
we're
looking
at
ops
and
nodejs
the
python
version
of
internally
so
we're
we're
writing
that
for
typescript
for
javascript
users
too
underpinning
and
no
gs
for
asynchronous
programming
we're
working
better
to
make
that
scheduler
work
much
nicer
so
we're
converter
script
and
the
newest
runtime
tensorflow
org
we
have
quite
a
bit
of
do
is
on
github
tf
tensorflow
/tf
gs
is
we've
pre
train
packaged
up
for
javascript
use
and
published
over
npm
a
lot
of
them
repo
and
that's
all

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