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

Machine Learning Research & Interpreting Neural Networks
觀看次數：319
this
distill
for
a
great
dissemination
these
you
know
you're
you're
so
you
acknowledged
as
a
real
sort
of
non-academic
community
but
it's
also
referential
if
that's
the
word
you
know
distills
also
peer-reviewed
and
and
full
of
interactive
diagrams
where
you
it's
just
it's
so
alien
to
me
it's
so
kind
of
three-quarters
of
the
way
up
the
grasp
some
of
the
concepts
and
and
right
oh
so
and
you're
using
distill
to
disseminate
lucid
could
you
tell
us
a
further
you
know
there's
there
there's
a
there's
a
lot
of
people
but
there's
a
neural
networks
really
work
and
how
many
visualizing
you
know
what
what
do
neural
they're
sort
of
they're
you
know
they're
real
artists
so
we're
doing
things
I
this
article
on
future
visualization
early
layers
it's
looking
for
edges
oh
and
logos
and
hexagons
and
you
know
keep
dreamin
not
just
asean
there's
been
networks
those
individual
neurons
that
fluffy
you
know
this
labrador
retriever
there's
real
Tigers
and
then
there's
labrador
retriever
either
I
know
the
look
at
the
vectors
and
then
the
neural
that
runs
the
theme
detectors
at
every
activation
vector
and
it's
just
a
list
inscrutable
you're
like
you
know
neuron
feature
visualizations
okay
that
give
us
dictionaries
where
we
have
you
know
fired
and
that's
that's
a
lot
more
floppy
eared
detector
really
these
two
floppy
ear
detectors
detectors
but
they
don't
fire
very
and
sunglasses
look
similar
right
it's
there's
an
second
but
there's
there's
some
other
that's
that's
the
first
step
um
we
we
so
we
you
know
there's
all
these
neurons
bunch
of
layers
they're
really
focused
on
relatively
you
know
combinations
of
edges
or
maybe
the
beginnings
of
textures
but
we
go
we're
skipping
a
layer
in
the
middle
chains
now
similarly
if
we
go
you
know
there's
fur
textures
there's
grass
textures
there's
you
know
this
you
can
where
there's
boundaries
and
sort
of
um
sophistication
of
understanding
the
illustrations
in
it
we
can
see
you
interfaces
um
i'm
sorry
I
go
able
to
there's
two
levels
of
opaqueness
with
it's
it's
a
black
box
right
it's
a
there's
a
there's
a
trained
graph
in
between
those
two
levels
of
opaqueness
this
paper
is
we're
also
open
sourcing
that
that
first
to
feature
visualization
know
that's
also
what
underlies
all
of
the
neurons
in
googly
nod
are
like
overhaul
so
you
can
go
get
hands-on
yeah
these
these
activation
grid
visualizations
and
then
you
can
go
and
these
concepts
these
frictions
these
tensorflow
slash
lucid
on
github
um
notebooks
right
um
or
or
after
you've

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