SlideShare une entreprise Scribd logo
1  sur  44
Télécharger pour lire hors ligne
TensorFlow深度學習快速上⼿手班

四、⾃自然語⾔言處理應⽤用	
By Mark Chang
•  ⾃自然語⾔言處理簡介	
•  Word2vec神經網路	
•  語意運算實作
⾃自然語⾔言處理簡介
⾃自然語⾔言處理	
•  ⾃自然語⾔言處理是⼈人⼯工智慧和語⾔言學領域的分⽀支	
– 探討如何處理及運⽤用⾃自然語⾔言	
•  ⾃自然語⾔言理解系統	
– 把⾃自然語⾔言轉化為電腦易於處理的形式。	
•  ⾃自然語⾔言⽣生成系統	
– 把電腦程式數據轉化為⾃自然語⾔言。	
•  https://zh.wikipedia.org/wiki/%E8%87%AA
%E7%84%B6%E8%AF%AD
%E8%A8%80%E5%A4%84%E7%90%86
語意理解	
https://papers.nips.cc/paper/5021-distributed-representations-of-
words-and-phrases-and-their-compositionality.pdf
機器翻譯	
http://arxiv.org/abs/1409.0473
詩詞創作	
http://emnlp2014.org/papers/pdf/EMNLP2014074.pdf
影像標題產⽣生	
http://arxiv.org/pdf/1411.4555v2.pdf
影像內容問答	
http://arxiv.org/pdf/1505.00468v6.pdf
Word2vec神經網路
⽂文字的語意	
•  某個字的語意,可從它的上下⽂文得知	
dog 和 cat 語意相近.
The dog run.
A cat run.
A dog sleep.
The cat sleep.
A dog bark.
The cat meows.
語意向量	
The dog run.
A cat run.
A dog sleep.
The cat sleep.
A dog bark.
The cat meows.
the a run sleep bark meow
dog 1 2 2 2 1 0
cat 2 1 2 2 0 1
語意向量	
dog (1, 2,..., xn)
cat (2, 1,..., xn)
Car (0, 0,..., xn)
語意向量相似度	
•  A 和 B 的Cosine Similarity 為:	
 A · B
|A||B|
dog (a1,a2,...,an)
cat (b1,b2,...,bn)
dog 和 cat 的cosine similarity為:
a1b1 + a2b2 + ... + anbn
p
a2
1 + a2
2 + ... + a2
n
p
b2
1 + b2
2 + ... + b2
n
語意向量加減運算	
Woman +
King - Man
= Queen
Woman
Queen
Man
 King
King - Man
King - Man
語意向量維度太⼤大	
(x1=the, x2 =a,..., xn)
dog
語意向量的維度等於總字彙量
x
1
x
2
x
3
x
4
xn...
Word2vec神經網路	
dog
One-Hot
Encoding
word2vec
神經網路
壓縮過的
語意向量
1.2
0.7
0.5
1
0
0
0
One-Hot Encoding	
dog
 cat
 run
 fly
1
0
0
0
0
1
0
0
0
0
1
0
0
0
0
1
Initialize Weights	
dog
cat
run
fly
dog
cat
run
fly
W =
2
6
6
4
w11 w12 w13
w21 w22 w23
w31 w32 w33
w31 w32 w43
3
7
7
5V =
2
6
6
4
v11 v12 v13
v21 v22 v23
v31 v32 v33
v31 v32 v43
3
7
7
5
把語意向量壓縮	
1
0
0
0
dog
高維度
低維度
v11
v12
v13
v11
v12
v13
v11
v12
v13
1
0
0
0
Compressed Vectors	
dog
 cat
 run
 fly
v11
v12
v13
v21
v22
v23
w31
w32
w33
w41
w42
w43
dog
cat
run
fly
dog
cat
run
fly
Context Word	
dog
 1
0
0
0
v11
v12
v13
v11
v12
v13 run
0
0
1
0
w31
w32
w33
dog
cat
run
fly
 dog
 cat
run
fly
1
1 + e V1W3
⇡ 1
V1 · W3 = v11w31 + v12w32 + v13w33
Context Word	
cat
1
0
0
0
v11
v12
v13
v21
v22
v23 run
0
0
1
0
w31
w32
w33
dog
 cat
run
fly
V2 · W3 = v21w31 + v22w32 + v23w33
dog
 cat
run
fly
1
1 + e V2W3
⇡ 1
Non-context Word	
dog
 1
0
0
0
v11
v12
v13
v11
v12
v13
fly
0
0
1
0
w41
w42
w43
V1 · W4 = v11w41 + v12w42 + v13w43
1
1 + e V1W4
⇡ 0
dog
 cat
run
fly
dog
 cat
run
fly
Non-context Word	
cat
 1
0
0
0
v11
v12
v13
v21
v22
v23
w41
w42
w43
0
0
1
0
V2 · W4 = v21w41 + v22w42 + v23w43
dog
 cat
run
fly
dog
 cat
run
fly
fly
1
1 + e V2W4
⇡ 0
Result	
dog
 cat
run
fly
dog
 cat
 run
 fly
v11
v12
v13
v21
v22
v23
w31
w32
w33
w41
w42
w43
dog
cat
run
fly
語意運算實作
語意運算實作	
https://github.com/ckmarkoh/ntc_deeplearning_tensorflow/
blob/master/sec4/semantics.ipynb
訓練資料	
anarchism originated as a term of abuse first used against early
working class radicals including the diggers of the english
revolution and the sans culottes of the french revolution whilst the
term is still used in a pejorative way to describe any act that used
violent means to destroy the organization of society it has also
been taken up as a positive label by self defined anarchists the
word anarchism is derived from the greek without archons ruler
chief king anarchism as a political philosophy is the belief that
rulers are unnecessary and should be abolished although there
are differing interpretations of what this means anarchism also
refers to related social movements that advocate the elimination
of authoritarian institutions particularly the state the word anarchy
as most anarchists use it does not imply chaos nihilism or anomie
but rather a harmonious anti authoritarian society in place of what
前處理	
anarchism originated as a
term of abuse first used
against early working class
radicals including the
diggers of the english
revolution and the sans
culottes of the french
revolution whilst the term is
still used in a pejorative way
to describe any act that used
violent means to destroy the
organization of society it has
also been taken up ….
[‘anarchism’, ‘originated’, ‘as’,
‘a’, ‘term’, ‘of’, ‘abuse’, ‘first’,
‘used’, ‘against’, ‘early’,
‘working’, ‘class’, ‘radicals’,
‘including’, ‘the’, ‘diggers’, ‘of’,
‘the’, ‘english’, ‘revolution’,
‘and’, ‘the’, ‘sans’, ‘culottes’,
‘of’, ‘the’, ‘french’, ‘revolution’,
‘whilst’, ‘the’, ‘term’, ‘is’, ‘still’,
‘used’, ‘in’, ‘a’, ‘pejorative’,
‘way’, ‘to’, ‘describe’, ‘any’,
‘act’, ‘that’, ‘used’, ‘violent’,
‘means’, ‘to’, ‘destroy’, ‘the’... ]
前處理	
‘the’, ‘english’,
‘revolution’,
‘and’, ‘the’,
‘sans’, UNK, 'of',
'the', 'french',
'revolution’…
1, 103,
855, 3, 1,
15068, 0,
2, 1, 151,
855, …
‘the’, ‘english’,
‘revolution’, ‘and’,
‘the’, ‘sans’,
‘culottes’, 'of', 'the',
'french',
'revolution’…
‘the’, ‘english’, ‘revolution’, ‘and’,
‘the’, ‘sans’, ‘culottes’, 'of', 'the',
'french', 'revolution’…
字典外的字,
用UNK代替。
將字轉換成字
典內的代碼。
根據詞頻,
轉換成字典
{“UNK”: 0, “the”: 1, “of”: 2, “and”:
3, “one”: 4, “in”: 5, “a”: 6, “to”: 7,
“zero”: 8, “nine”: 9, .... }
# 字典大小
vocabulary_size = 50000
前處理	
5239, 3084, 12, 6, 195, 2, 3137, 46,
59, 156, 128, 742, 477, 10572, 134,
1, 27549, 2, 1, 103, 855, 3, 1, 15068,
0, 2, 1, 151, 855, …
input output
3084 5239
3084 12
12 3084
12 6
6 12
6 195
195 6
195 2
3084 5239
word2vec
前處理	
5239, 3084, 12, 6, 195, 2, 3137, 46, 59, 156, 128,
742, 477, 10572, 134, 1, 27549, 2, 1, 103, 855, 3, 1,
15068, 0, 2, 1, 151, 855, …
generate_batch(batch_size=8, num_skips=2, skip_window=1)
batch
size
input 3084 3084 12 12 6 6 195 195
output 5239 12 3084 6 12 195 6 2
num_skips
batch_size
skip_window=1
Computational Graph	
train_inputs = tf.placeholder(tf.int32, shape=[batch_size])
train_labels = tf.placeholder(tf.int32, shape=[batch_size, 1])
with tf.device('/cpu:0'):
embeddings = tf.Variable(
tf.random_uniform([vocabulary_size, embedding_size], -1.0, 1.0))
embed = tf.nn.embedding_lookup(embeddings, train_inputs)
nce_weights = tf.Variable(
tf.truncated_normal([vocabulary_size, embedding_size],
stddev=1.0 / math.sqrt(embedding_size)))
nce_biases = tf.Variable(tf.zeros([vocabulary_size]))
loss = tf.reduce_mean(
tf.nn.nce_loss(nce_weights, nce_biases, embed, train_labels,
num_sampled, vocabulary_size))
optimizer = tf.train.GradientDescentOptimizer(1.0).minimize(loss)
Device	
with tf.device('/cpu:0’)
在CPU上執行以下定義的Computational Graph
由於Tensorflow未支援 embedding_lookup 在GPU上
執行,故需令它在CPU上執行。
Inputs  Outputs	
word2vec
train_inputs = tf.placeholder(tf.int32, shape=[batch_size])
train_labels = tf.placeholder(tf.int32, shape=[batch_size, 1])
train_inputs
3084
3084
12
12
6
6
195
195
train_labels
5239
12
3084
6
12
195
6
2
Embedding Lookup	
embeddings = tf.Variable(tf.random_uniform([vocabulary_size,
embedding_size], -1.0, 1.0))
embed = tf.nn.embedding_lookup(embeddings, train_inputs)
train_inputs
2
embeddings
embedding_lookup
NCE Weights	
•  NCE: Noise Contrastive Estimation	
nce_weights = tf.Variable(
tf.truncated_normal([vocabulary_size,
embedding_size],
stddev=1.0 / math.sqrt(embedding_size)
))
nce_biases = tf.Variable( tf.zeros([vocabulary_size]) )
nce_weights
nce_biases
NCE Loss	
loss = tf.reduce_mean(
tf.nn.nce_loss(nce_weights, nce_biases, embed, train_labels,
num_sampled, vocabulary_size))
1
0
0
0
v11
v12
v13
v21
v22
v23
0
0
1
0
w31
w32
w33
1
1 + e V2W3
⇡ 1
1
0
0
0
v11
v12
v13
v21
v22
v23
w41
w42
w43
0
0
1
0
1
1 + e V2W4
⇡ 0
Positive Negative
cost = log(
1
1 + e vT
I wpos
) +
X
neg
log(1
1
1 + e vT
I wneg
)
Train	
feed_dict = {train_inputs: batch_inputs,
train_labels: batch_labels}
_, loss_val = session.run([optimizer, loss], feed_dict=feed_dict)
loss_val
batch_inputs
3084
3084
12
12
6
6
195
195
batch_labels
5239
12
3084
6
12
195
6
2
Result	
final_embeddings
array([[-0.02782757, -0.16879494, -0.06111901, ..., -0.25700757,
-0.07137159, 0.0191142 ],
[-0.00155336, -0.00928817, -0.0535327 , ..., -0.23261793,
-0.13980433, 0.18055709],
[ 0.02576068, -0.06805354, -0.03688766, ..., -0.15378961,
0.00459271, 0.0717089 ],
...,
[ 0.01061165, -0.09820389, -0.09913248, ..., 0.00818674,
-0.12992384, 0.05826835],
[ 0.0849214 , -0.14137401, 0.09674817, ..., 0.04111136,
-0.05420518, -0.01920278],
[ 0.08318492, -0.08202577, 0.11284919, ..., 0.03887166,
0.01556483, 0.12496017]], dtype=float32)
Visualization
Most Similar Words	
def get_most_similar(word, top=10):
wid = dictionary.get(word,-1)
result = np.dot(final_embeddings[wid:wid+1,:],final_embeddings.T)
result = result [0].argsort().tolist()
result.reverse()
for idx in result [:10]:
print(reverse_dictionary[idx])
get_most_similar(one)
one
six
two
four
seven
three
...
講師資訊	
•  Email: ckmarkoh at gmail dot com	
•  Blog: http://cpmarkchang.logdown.com	
•  Github: https://github.com/ckmarkoh	
Mark Chang
•  Facebook: https://www.facebook.com/ckmarkoh.chang
•  Slideshare: http://www.slideshare.net/ckmarkohchang
•  Linkedin:
https://www.linkedin.com/pub/mark-chang/85/25b/847
44

Contenu connexe

Tendances

Deep Learning in your Browser: powered by WebGL
Deep Learning in your Browser: powered by WebGLDeep Learning in your Browser: powered by WebGL
Deep Learning in your Browser: powered by WebGLOswald Campesato
 
Rajat Monga at AI Frontiers: Deep Learning with TensorFlow
Rajat Monga at AI Frontiers: Deep Learning with TensorFlowRajat Monga at AI Frontiers: Deep Learning with TensorFlow
Rajat Monga at AI Frontiers: Deep Learning with TensorFlowAI Frontiers
 
Learning stochastic neural networks with Chainer
Learning stochastic neural networks with ChainerLearning stochastic neural networks with Chainer
Learning stochastic neural networks with ChainerSeiya Tokui
 
Introduction to Machine Learning with TensorFlow
Introduction to Machine Learning with TensorFlowIntroduction to Machine Learning with TensorFlow
Introduction to Machine Learning with TensorFlowPaolo Tomeo
 
Simple APIs and innovative documentation
Simple APIs and innovative documentationSimple APIs and innovative documentation
Simple APIs and innovative documentationPyDataParis
 
Deep Learning with TensorFlow: Understanding Tensors, Computations Graphs, Im...
Deep Learning with TensorFlow: Understanding Tensors, Computations Graphs, Im...Deep Learning with TensorFlow: Understanding Tensors, Computations Graphs, Im...
Deep Learning with TensorFlow: Understanding Tensors, Computations Graphs, Im...Altoros
 
Introduction to Deep Learning, Keras, and TensorFlow
Introduction to Deep Learning, Keras, and TensorFlowIntroduction to Deep Learning, Keras, and TensorFlow
Introduction to Deep Learning, Keras, and TensorFlowSri Ambati
 
Introduction to Neural Networks in Tensorflow
Introduction to Neural Networks in TensorflowIntroduction to Neural Networks in Tensorflow
Introduction to Neural Networks in TensorflowNicholas McClure
 
Pythonで機械学習入門以前
Pythonで機械学習入門以前Pythonで機械学習入門以前
Pythonで機械学習入門以前Kimikazu Kato
 
Warsaw Data Science - Factorization Machines Introduction
Warsaw Data Science -  Factorization Machines IntroductionWarsaw Data Science -  Factorization Machines Introduction
Warsaw Data Science - Factorization Machines IntroductionBartlomiej Twardowski
 
Factorization Machines and Applications in Recommender Systems
Factorization Machines and Applications in Recommender SystemsFactorization Machines and Applications in Recommender Systems
Factorization Machines and Applications in Recommender SystemsEvgeniy Marinov
 
Convolutional neural network in practice
Convolutional neural network in practiceConvolutional neural network in practice
Convolutional neural network in practice남주 김
 
Hack Like It's 2013 (The Workshop)
Hack Like It's 2013 (The Workshop)Hack Like It's 2013 (The Workshop)
Hack Like It's 2013 (The Workshop)Itzik Kotler
 
NIPS読み会2013: One-shot learning by inverting a compositional causal process
NIPS読み会2013: One-shot learning by inverting  a compositional causal processNIPS読み会2013: One-shot learning by inverting  a compositional causal process
NIPS読み会2013: One-shot learning by inverting a compositional causal processnozyh
 
[신경망기초]오류역전파알고리즘구현
[신경망기초]오류역전파알고리즘구현[신경망기초]오류역전파알고리즘구현
[신경망기초]오류역전파알고리즘구현jaypi Ko
 

Tendances (18)

Deep Learning in your Browser: powered by WebGL
Deep Learning in your Browser: powered by WebGLDeep Learning in your Browser: powered by WebGL
Deep Learning in your Browser: powered by WebGL
 
Python book
Python bookPython book
Python book
 
Rajat Monga at AI Frontiers: Deep Learning with TensorFlow
Rajat Monga at AI Frontiers: Deep Learning with TensorFlowRajat Monga at AI Frontiers: Deep Learning with TensorFlow
Rajat Monga at AI Frontiers: Deep Learning with TensorFlow
 
Learning stochastic neural networks with Chainer
Learning stochastic neural networks with ChainerLearning stochastic neural networks with Chainer
Learning stochastic neural networks with Chainer
 
Introduction to Machine Learning with TensorFlow
Introduction to Machine Learning with TensorFlowIntroduction to Machine Learning with TensorFlow
Introduction to Machine Learning with TensorFlow
 
Simple APIs and innovative documentation
Simple APIs and innovative documentationSimple APIs and innovative documentation
Simple APIs and innovative documentation
 
Deep Learning with TensorFlow: Understanding Tensors, Computations Graphs, Im...
Deep Learning with TensorFlow: Understanding Tensors, Computations Graphs, Im...Deep Learning with TensorFlow: Understanding Tensors, Computations Graphs, Im...
Deep Learning with TensorFlow: Understanding Tensors, Computations Graphs, Im...
 
Introduction to Deep Learning, Keras, and TensorFlow
Introduction to Deep Learning, Keras, and TensorFlowIntroduction to Deep Learning, Keras, and TensorFlow
Introduction to Deep Learning, Keras, and TensorFlow
 
Introduction to Neural Networks in Tensorflow
Introduction to Neural Networks in TensorflowIntroduction to Neural Networks in Tensorflow
Introduction to Neural Networks in Tensorflow
 
Pythonで機械学習入門以前
Pythonで機械学習入門以前Pythonで機械学習入門以前
Pythonで機械学習入門以前
 
Warsaw Data Science - Factorization Machines Introduction
Warsaw Data Science -  Factorization Machines IntroductionWarsaw Data Science -  Factorization Machines Introduction
Warsaw Data Science - Factorization Machines Introduction
 
Factorization Machines and Applications in Recommender Systems
Factorization Machines and Applications in Recommender SystemsFactorization Machines and Applications in Recommender Systems
Factorization Machines and Applications in Recommender Systems
 
Convolutional neural network in practice
Convolutional neural network in practiceConvolutional neural network in practice
Convolutional neural network in practice
 
Hack Like It's 2013 (The Workshop)
Hack Like It's 2013 (The Workshop)Hack Like It's 2013 (The Workshop)
Hack Like It's 2013 (The Workshop)
 
Sparse autoencoder
Sparse autoencoderSparse autoencoder
Sparse autoencoder
 
10 simulation
10 simulation10 simulation
10 simulation
 
NIPS読み会2013: One-shot learning by inverting a compositional causal process
NIPS読み会2013: One-shot learning by inverting  a compositional causal processNIPS読み会2013: One-shot learning by inverting  a compositional causal process
NIPS読み会2013: One-shot learning by inverting a compositional causal process
 
[신경망기초]오류역전파알고리즘구현
[신경망기초]오류역전파알고리즘구현[신경망기초]오류역전파알고리즘구현
[신경망기초]오류역전파알고리즘구현
 

En vedette

TensorFlow 深度學習講座
TensorFlow 深度學習講座TensorFlow 深度學習講座
TensorFlow 深度學習講座Mark Chang
 
TensorFlow 深度學習快速上手班--深度學習
 TensorFlow 深度學習快速上手班--深度學習 TensorFlow 深度學習快速上手班--深度學習
TensorFlow 深度學習快速上手班--深度學習Mark Chang
 
淺談深度學習
淺談深度學習淺談深度學習
淺談深度學習Mark Chang
 
Generative Adversarial Networks
Generative Adversarial NetworksGenerative Adversarial Networks
Generative Adversarial NetworksMark Chang
 
The Genome Assembly Problem
The Genome Assembly ProblemThe Genome Assembly Problem
The Genome Assembly ProblemMark Chang
 
Applied Deep Learning 11/03 Convolutional Neural Networks
Applied Deep Learning 11/03 Convolutional Neural NetworksApplied Deep Learning 11/03 Convolutional Neural Networks
Applied Deep Learning 11/03 Convolutional Neural NetworksMark Chang
 
自然語言處理簡介
自然語言處理簡介自然語言處理簡介
自然語言處理簡介Mark Chang
 
Variational Autoencoder
Variational AutoencoderVariational Autoencoder
Variational AutoencoderMark Chang
 
以深度學習加速語音及影像辨識應用發展
以深度學習加速語音及影像辨識應用發展以深度學習加速語音及影像辨識應用發展
以深度學習加速語音及影像辨識應用發展NVIDIA Taiwan
 
Computational Linguistics week 10
 Computational Linguistics week 10 Computational Linguistics week 10
Computational Linguistics week 10Mark Chang
 
Neural Art (English Version)
Neural Art (English Version)Neural Art (English Version)
Neural Art (English Version)Mark Chang
 
AlphaGo in Depth
AlphaGo in Depth AlphaGo in Depth
AlphaGo in Depth Mark Chang
 
Computational Linguistics week 5
Computational Linguistics  week 5Computational Linguistics  week 5
Computational Linguistics week 5Mark Chang
 
DRAW: Deep Recurrent Attentive Writer
DRAW: Deep Recurrent Attentive WriterDRAW: Deep Recurrent Attentive Writer
DRAW: Deep Recurrent Attentive WriterMark Chang
 
民主的網路世代—推動罷免的工程師們
民主的網路世代—推動罷免的工程師們民主的網路世代—推動罷免的工程師們
民主的網路世代—推動罷免的工程師們Mark Chang
 
Large Scale Deep Learning with TensorFlow
Large Scale Deep Learning with TensorFlow Large Scale Deep Learning with TensorFlow
Large Scale Deep Learning with TensorFlow Jen Aman
 
[系列活動] 手把手的深度學習實務
[系列活動] 手把手的深度學習實務[系列活動] 手把手的深度學習實務
[系列活動] 手把手的深度學習實務台灣資料科學年會
 

En vedette (20)

TensorFlow 深度學習講座
TensorFlow 深度學習講座TensorFlow 深度學習講座
TensorFlow 深度學習講座
 
TensorFlow 深度學習快速上手班--深度學習
 TensorFlow 深度學習快速上手班--深度學習 TensorFlow 深度學習快速上手班--深度學習
TensorFlow 深度學習快速上手班--深度學習
 
TENSORFLOW深度學習講座講義(很硬的課程)
TENSORFLOW深度學習講座講義(很硬的課程)TENSORFLOW深度學習講座講義(很硬的課程)
TENSORFLOW深度學習講座講義(很硬的課程)
 
淺談深度學習
淺談深度學習淺談深度學習
淺談深度學習
 
Generative Adversarial Networks
Generative Adversarial NetworksGenerative Adversarial Networks
Generative Adversarial Networks
 
The Genome Assembly Problem
The Genome Assembly ProblemThe Genome Assembly Problem
The Genome Assembly Problem
 
Applied Deep Learning 11/03 Convolutional Neural Networks
Applied Deep Learning 11/03 Convolutional Neural NetworksApplied Deep Learning 11/03 Convolutional Neural Networks
Applied Deep Learning 11/03 Convolutional Neural Networks
 
自然語言處理簡介
自然語言處理簡介自然語言處理簡介
自然語言處理簡介
 
Neural Doodle
Neural DoodleNeural Doodle
Neural Doodle
 
Variational Autoencoder
Variational AutoencoderVariational Autoencoder
Variational Autoencoder
 
以深度學習加速語音及影像辨識應用發展
以深度學習加速語音及影像辨識應用發展以深度學習加速語音及影像辨識應用發展
以深度學習加速語音及影像辨識應用發展
 
Computational Linguistics week 10
 Computational Linguistics week 10 Computational Linguistics week 10
Computational Linguistics week 10
 
Neural Art (English Version)
Neural Art (English Version)Neural Art (English Version)
Neural Art (English Version)
 
AlphaGo in Depth
AlphaGo in Depth AlphaGo in Depth
AlphaGo in Depth
 
Computational Linguistics week 5
Computational Linguistics  week 5Computational Linguistics  week 5
Computational Linguistics week 5
 
DRAW: Deep Recurrent Attentive Writer
DRAW: Deep Recurrent Attentive WriterDRAW: Deep Recurrent Attentive Writer
DRAW: Deep Recurrent Attentive Writer
 
TensorFlow
TensorFlowTensorFlow
TensorFlow
 
民主的網路世代—推動罷免的工程師們
民主的網路世代—推動罷免的工程師們民主的網路世代—推動罷免的工程師們
民主的網路世代—推動罷免的工程師們
 
Large Scale Deep Learning with TensorFlow
Large Scale Deep Learning with TensorFlow Large Scale Deep Learning with TensorFlow
Large Scale Deep Learning with TensorFlow
 
[系列活動] 手把手的深度學習實務
[系列活動] 手把手的深度學習實務[系列活動] 手把手的深度學習實務
[系列活動] 手把手的深度學習實務
 

Similaire à TensorFlow 深度學習快速上手班--自然語言處理應用

Software version numbering - DSL of change
Software version numbering - DSL of changeSoftware version numbering - DSL of change
Software version numbering - DSL of changeSergii Shmarkatiuk
 
ICPSR - Complex Systems Models in the Social Sciences - Lab Session 1 - Profe...
ICPSR - Complex Systems Models in the Social Sciences - Lab Session 1 - Profe...ICPSR - Complex Systems Models in the Social Sciences - Lab Session 1 - Profe...
ICPSR - Complex Systems Models in the Social Sciences - Lab Session 1 - Profe...Daniel Katz
 
Design Patterns
Design PatternsDesign Patterns
Design Patternsppd1961
 
5 things to look for in a CMS interface
5 things to look for in a CMS interface5 things to look for in a CMS interface
5 things to look for in a CMS interfaceAlastair Campbell
 
Language translation with Deep Learning (RNN) with TensorFlow
Language translation with Deep Learning (RNN) with TensorFlowLanguage translation with Deep Learning (RNN) with TensorFlow
Language translation with Deep Learning (RNN) with TensorFlowS N
 
And Then There Are Algorithms
And Then There Are AlgorithmsAnd Then There Are Algorithms
And Then There Are AlgorithmsInfluxData
 
Discovering User's Topics of Interest in Recommender Systems @ Meetup Machine...
Discovering User's Topics of Interest in Recommender Systems @ Meetup Machine...Discovering User's Topics of Interest in Recommender Systems @ Meetup Machine...
Discovering User's Topics of Interest in Recommender Systems @ Meetup Machine...Gabriel Moreira
 
Threading Is Not A Model
Threading Is Not A ModelThreading Is Not A Model
Threading Is Not A Modelguest2a5acfb
 
the productive programer: mechanics
the productive programer: mechanicsthe productive programer: mechanics
the productive programer: mechanicselliando dias
 
Generating Natural-Language Text with Neural Networks
Generating Natural-Language Text with Neural NetworksGenerating Natural-Language Text with Neural Networks
Generating Natural-Language Text with Neural NetworksJonathan Mugan
 
Why should you care about Processing?
Why should you care about Processing?Why should you care about Processing?
Why should you care about Processing?Jamie Matthews
 
Intuition & Use-Cases of Embeddings in NLP & beyond
Intuition & Use-Cases of Embeddings in NLP & beyondIntuition & Use-Cases of Embeddings in NLP & beyond
Intuition & Use-Cases of Embeddings in NLP & beyondC4Media
 
Patterns for organic architecture codedive
Patterns for organic architecture codedivePatterns for organic architecture codedive
Patterns for organic architecture codedivemagda3695
 
Detecting Misleading Headlines in Online News: Hands-on Experiences on Attent...
Detecting Misleading Headlines in Online News: Hands-on Experiences on Attent...Detecting Misleading Headlines in Online News: Hands-on Experiences on Attent...
Detecting Misleading Headlines in Online News: Hands-on Experiences on Attent...Kunwoo Park
 
Computer and Generations
Computer and GenerationsComputer and Generations
Computer and GenerationsToufiqueAhmed13
 

Similaire à TensorFlow 深度學習快速上手班--自然語言處理應用 (20)

Software version numbering - DSL of change
Software version numbering - DSL of changeSoftware version numbering - DSL of change
Software version numbering - DSL of change
 
ICPSR - Complex Systems Models in the Social Sciences - Lab Session 1 - Profe...
ICPSR - Complex Systems Models in the Social Sciences - Lab Session 1 - Profe...ICPSR - Complex Systems Models in the Social Sciences - Lab Session 1 - Profe...
ICPSR - Complex Systems Models in the Social Sciences - Lab Session 1 - Profe...
 
Design Patterns
Design PatternsDesign Patterns
Design Patterns
 
5 things to look for in a CMS interface
5 things to look for in a CMS interface5 things to look for in a CMS interface
5 things to look for in a CMS interface
 
Language translation with Deep Learning (RNN) with TensorFlow
Language translation with Deep Learning (RNN) with TensorFlowLanguage translation with Deep Learning (RNN) with TensorFlow
Language translation with Deep Learning (RNN) with TensorFlow
 
Python: The Dynamic!
Python: The Dynamic!Python: The Dynamic!
Python: The Dynamic!
 
And Then There Are Algorithms
And Then There Are AlgorithmsAnd Then There Are Algorithms
And Then There Are Algorithms
 
Discovering User's Topics of Interest in Recommender Systems @ Meetup Machine...
Discovering User's Topics of Interest in Recommender Systems @ Meetup Machine...Discovering User's Topics of Interest in Recommender Systems @ Meetup Machine...
Discovering User's Topics of Interest in Recommender Systems @ Meetup Machine...
 
Of Changes and Their History
Of Changes and Their HistoryOf Changes and Their History
Of Changes and Their History
 
Threading Is Not A Model
Threading Is Not A ModelThreading Is Not A Model
Threading Is Not A Model
 
Project00
Project00Project00
Project00
 
the productive programer: mechanics
the productive programer: mechanicsthe productive programer: mechanics
the productive programer: mechanics
 
Generating Natural-Language Text with Neural Networks
Generating Natural-Language Text with Neural NetworksGenerating Natural-Language Text with Neural Networks
Generating Natural-Language Text with Neural Networks
 
Travel Essay Examples
Travel Essay ExamplesTravel Essay Examples
Travel Essay Examples
 
Travel Essay Examples.pdf
Travel Essay Examples.pdfTravel Essay Examples.pdf
Travel Essay Examples.pdf
 
Why should you care about Processing?
Why should you care about Processing?Why should you care about Processing?
Why should you care about Processing?
 
Intuition & Use-Cases of Embeddings in NLP & beyond
Intuition & Use-Cases of Embeddings in NLP & beyondIntuition & Use-Cases of Embeddings in NLP & beyond
Intuition & Use-Cases of Embeddings in NLP & beyond
 
Patterns for organic architecture codedive
Patterns for organic architecture codedivePatterns for organic architecture codedive
Patterns for organic architecture codedive
 
Detecting Misleading Headlines in Online News: Hands-on Experiences on Attent...
Detecting Misleading Headlines in Online News: Hands-on Experiences on Attent...Detecting Misleading Headlines in Online News: Hands-on Experiences on Attent...
Detecting Misleading Headlines in Online News: Hands-on Experiences on Attent...
 
Computer and Generations
Computer and GenerationsComputer and Generations
Computer and Generations
 

Plus de Mark Chang

Modeling the Dynamics of SGD by Stochastic Differential Equation
Modeling the Dynamics of SGD by Stochastic Differential EquationModeling the Dynamics of SGD by Stochastic Differential Equation
Modeling the Dynamics of SGD by Stochastic Differential EquationMark Chang
 
Modeling the Dynamics of SGD by Stochastic Differential Equation
Modeling the Dynamics of SGD by Stochastic Differential EquationModeling the Dynamics of SGD by Stochastic Differential Equation
Modeling the Dynamics of SGD by Stochastic Differential EquationMark Chang
 
Information in the Weights
Information in the WeightsInformation in the Weights
Information in the WeightsMark Chang
 
Information in the Weights
Information in the WeightsInformation in the Weights
Information in the WeightsMark Chang
 
PAC Bayesian for Deep Learning
PAC Bayesian for Deep LearningPAC Bayesian for Deep Learning
PAC Bayesian for Deep LearningMark Chang
 
PAC-Bayesian Bound for Deep Learning
PAC-Bayesian Bound for Deep LearningPAC-Bayesian Bound for Deep Learning
PAC-Bayesian Bound for Deep LearningMark Chang
 
Domain Adaptation
Domain AdaptationDomain Adaptation
Domain AdaptationMark Chang
 
NTU ML TENSORFLOW
NTU ML TENSORFLOWNTU ML TENSORFLOW
NTU ML TENSORFLOWMark Chang
 
NTHU AI Reading Group: Improved Training of Wasserstein GANs
NTHU AI Reading Group: Improved Training of Wasserstein GANsNTHU AI Reading Group: Improved Training of Wasserstein GANs
NTHU AI Reading Group: Improved Training of Wasserstein GANsMark Chang
 
Image completion
Image completionImage completion
Image completionMark Chang
 
NeuralArt 電腦作畫
NeuralArt 電腦作畫NeuralArt 電腦作畫
NeuralArt 電腦作畫Mark Chang
 
Language Understanding for Text-based Games using Deep Reinforcement Learning
Language Understanding for Text-based Games using Deep Reinforcement LearningLanguage Understanding for Text-based Games using Deep Reinforcement Learning
Language Understanding for Text-based Games using Deep Reinforcement LearningMark Chang
 
Discourse Representation Theory
Discourse Representation TheoryDiscourse Representation Theory
Discourse Representation TheoryMark Chang
 

Plus de Mark Chang (13)

Modeling the Dynamics of SGD by Stochastic Differential Equation
Modeling the Dynamics of SGD by Stochastic Differential EquationModeling the Dynamics of SGD by Stochastic Differential Equation
Modeling the Dynamics of SGD by Stochastic Differential Equation
 
Modeling the Dynamics of SGD by Stochastic Differential Equation
Modeling the Dynamics of SGD by Stochastic Differential EquationModeling the Dynamics of SGD by Stochastic Differential Equation
Modeling the Dynamics of SGD by Stochastic Differential Equation
 
Information in the Weights
Information in the WeightsInformation in the Weights
Information in the Weights
 
Information in the Weights
Information in the WeightsInformation in the Weights
Information in the Weights
 
PAC Bayesian for Deep Learning
PAC Bayesian for Deep LearningPAC Bayesian for Deep Learning
PAC Bayesian for Deep Learning
 
PAC-Bayesian Bound for Deep Learning
PAC-Bayesian Bound for Deep LearningPAC-Bayesian Bound for Deep Learning
PAC-Bayesian Bound for Deep Learning
 
Domain Adaptation
Domain AdaptationDomain Adaptation
Domain Adaptation
 
NTU ML TENSORFLOW
NTU ML TENSORFLOWNTU ML TENSORFLOW
NTU ML TENSORFLOW
 
NTHU AI Reading Group: Improved Training of Wasserstein GANs
NTHU AI Reading Group: Improved Training of Wasserstein GANsNTHU AI Reading Group: Improved Training of Wasserstein GANs
NTHU AI Reading Group: Improved Training of Wasserstein GANs
 
Image completion
Image completionImage completion
Image completion
 
NeuralArt 電腦作畫
NeuralArt 電腦作畫NeuralArt 電腦作畫
NeuralArt 電腦作畫
 
Language Understanding for Text-based Games using Deep Reinforcement Learning
Language Understanding for Text-based Games using Deep Reinforcement LearningLanguage Understanding for Text-based Games using Deep Reinforcement Learning
Language Understanding for Text-based Games using Deep Reinforcement Learning
 
Discourse Representation Theory
Discourse Representation TheoryDiscourse Representation Theory
Discourse Representation Theory
 

Dernier

Architecting Cloud Native Applications
Architecting Cloud Native ApplicationsArchitecting Cloud Native Applications
Architecting Cloud Native ApplicationsWSO2
 
Apidays New York 2024 - Scaling API-first by Ian Reasor and Radu Cotescu, Adobe
Apidays New York 2024 - Scaling API-first by Ian Reasor and Radu Cotescu, AdobeApidays New York 2024 - Scaling API-first by Ian Reasor and Radu Cotescu, Adobe
Apidays New York 2024 - Scaling API-first by Ian Reasor and Radu Cotescu, Adobeapidays
 
Exploring Multimodal Embeddings with Milvus
Exploring Multimodal Embeddings with MilvusExploring Multimodal Embeddings with Milvus
Exploring Multimodal Embeddings with MilvusZilliz
 
FWD Group - Insurer Innovation Award 2024
FWD Group - Insurer Innovation Award 2024FWD Group - Insurer Innovation Award 2024
FWD Group - Insurer Innovation Award 2024The Digital Insurer
 
How to Troubleshoot Apps for the Modern Connected Worker
How to Troubleshoot Apps for the Modern Connected WorkerHow to Troubleshoot Apps for the Modern Connected Worker
How to Troubleshoot Apps for the Modern Connected WorkerThousandEyes
 
Apidays New York 2024 - Passkeys: Developing APIs to enable passwordless auth...
Apidays New York 2024 - Passkeys: Developing APIs to enable passwordless auth...Apidays New York 2024 - Passkeys: Developing APIs to enable passwordless auth...
Apidays New York 2024 - Passkeys: Developing APIs to enable passwordless auth...apidays
 
ProductAnonymous-April2024-WinProductDiscovery-MelissaKlemke
ProductAnonymous-April2024-WinProductDiscovery-MelissaKlemkeProductAnonymous-April2024-WinProductDiscovery-MelissaKlemke
ProductAnonymous-April2024-WinProductDiscovery-MelissaKlemkeProduct Anonymous
 
EMPOWERMENT TECHNOLOGY GRADE 11 QUARTER 2 REVIEWER
EMPOWERMENT TECHNOLOGY GRADE 11 QUARTER 2 REVIEWEREMPOWERMENT TECHNOLOGY GRADE 11 QUARTER 2 REVIEWER
EMPOWERMENT TECHNOLOGY GRADE 11 QUARTER 2 REVIEWERMadyBayot
 
Finding Java's Hidden Performance Traps @ DevoxxUK 2024
Finding Java's Hidden Performance Traps @ DevoxxUK 2024Finding Java's Hidden Performance Traps @ DevoxxUK 2024
Finding Java's Hidden Performance Traps @ DevoxxUK 2024Victor Rentea
 
CNIC Information System with Pakdata Cf In Pakistan
CNIC Information System with Pakdata Cf In PakistanCNIC Information System with Pakdata Cf In Pakistan
CNIC Information System with Pakdata Cf In Pakistandanishmna97
 
Polkadot JAM Slides - Token2049 - By Dr. Gavin Wood
Polkadot JAM Slides - Token2049 - By Dr. Gavin WoodPolkadot JAM Slides - Token2049 - By Dr. Gavin Wood
Polkadot JAM Slides - Token2049 - By Dr. Gavin WoodJuan lago vázquez
 
Artificial Intelligence Chap.5 : Uncertainty
Artificial Intelligence Chap.5 : UncertaintyArtificial Intelligence Chap.5 : Uncertainty
Artificial Intelligence Chap.5 : UncertaintyKhushali Kathiriya
 
DBX First Quarter 2024 Investor Presentation
DBX First Quarter 2024 Investor PresentationDBX First Quarter 2024 Investor Presentation
DBX First Quarter 2024 Investor PresentationDropbox
 
MINDCTI Revenue Release Quarter One 2024
MINDCTI Revenue Release Quarter One 2024MINDCTI Revenue Release Quarter One 2024
MINDCTI Revenue Release Quarter One 2024MIND CTI
 
Why Teams call analytics are critical to your entire business
Why Teams call analytics are critical to your entire businessWhy Teams call analytics are critical to your entire business
Why Teams call analytics are critical to your entire businesspanagenda
 
Apidays New York 2024 - The value of a flexible API Management solution for O...
Apidays New York 2024 - The value of a flexible API Management solution for O...Apidays New York 2024 - The value of a flexible API Management solution for O...
Apidays New York 2024 - The value of a flexible API Management solution for O...apidays
 
[BuildWithAI] Introduction to Gemini.pdf
[BuildWithAI] Introduction to Gemini.pdf[BuildWithAI] Introduction to Gemini.pdf
[BuildWithAI] Introduction to Gemini.pdfSandro Moreira
 
Manulife - Insurer Transformation Award 2024
Manulife - Insurer Transformation Award 2024Manulife - Insurer Transformation Award 2024
Manulife - Insurer Transformation Award 2024The Digital Insurer
 
AWS Community Day CPH - Three problems of Terraform
AWS Community Day CPH - Three problems of TerraformAWS Community Day CPH - Three problems of Terraform
AWS Community Day CPH - Three problems of TerraformAndrey Devyatkin
 

Dernier (20)

Architecting Cloud Native Applications
Architecting Cloud Native ApplicationsArchitecting Cloud Native Applications
Architecting Cloud Native Applications
 
Apidays New York 2024 - Scaling API-first by Ian Reasor and Radu Cotescu, Adobe
Apidays New York 2024 - Scaling API-first by Ian Reasor and Radu Cotescu, AdobeApidays New York 2024 - Scaling API-first by Ian Reasor and Radu Cotescu, Adobe
Apidays New York 2024 - Scaling API-first by Ian Reasor and Radu Cotescu, Adobe
 
Exploring Multimodal Embeddings with Milvus
Exploring Multimodal Embeddings with MilvusExploring Multimodal Embeddings with Milvus
Exploring Multimodal Embeddings with Milvus
 
FWD Group - Insurer Innovation Award 2024
FWD Group - Insurer Innovation Award 2024FWD Group - Insurer Innovation Award 2024
FWD Group - Insurer Innovation Award 2024
 
How to Troubleshoot Apps for the Modern Connected Worker
How to Troubleshoot Apps for the Modern Connected WorkerHow to Troubleshoot Apps for the Modern Connected Worker
How to Troubleshoot Apps for the Modern Connected Worker
 
Apidays New York 2024 - Passkeys: Developing APIs to enable passwordless auth...
Apidays New York 2024 - Passkeys: Developing APIs to enable passwordless auth...Apidays New York 2024 - Passkeys: Developing APIs to enable passwordless auth...
Apidays New York 2024 - Passkeys: Developing APIs to enable passwordless auth...
 
ProductAnonymous-April2024-WinProductDiscovery-MelissaKlemke
ProductAnonymous-April2024-WinProductDiscovery-MelissaKlemkeProductAnonymous-April2024-WinProductDiscovery-MelissaKlemke
ProductAnonymous-April2024-WinProductDiscovery-MelissaKlemke
 
EMPOWERMENT TECHNOLOGY GRADE 11 QUARTER 2 REVIEWER
EMPOWERMENT TECHNOLOGY GRADE 11 QUARTER 2 REVIEWEREMPOWERMENT TECHNOLOGY GRADE 11 QUARTER 2 REVIEWER
EMPOWERMENT TECHNOLOGY GRADE 11 QUARTER 2 REVIEWER
 
Finding Java's Hidden Performance Traps @ DevoxxUK 2024
Finding Java's Hidden Performance Traps @ DevoxxUK 2024Finding Java's Hidden Performance Traps @ DevoxxUK 2024
Finding Java's Hidden Performance Traps @ DevoxxUK 2024
 
CNIC Information System with Pakdata Cf In Pakistan
CNIC Information System with Pakdata Cf In PakistanCNIC Information System with Pakdata Cf In Pakistan
CNIC Information System with Pakdata Cf In Pakistan
 
Polkadot JAM Slides - Token2049 - By Dr. Gavin Wood
Polkadot JAM Slides - Token2049 - By Dr. Gavin WoodPolkadot JAM Slides - Token2049 - By Dr. Gavin Wood
Polkadot JAM Slides - Token2049 - By Dr. Gavin Wood
 
Artificial Intelligence Chap.5 : Uncertainty
Artificial Intelligence Chap.5 : UncertaintyArtificial Intelligence Chap.5 : Uncertainty
Artificial Intelligence Chap.5 : Uncertainty
 
DBX First Quarter 2024 Investor Presentation
DBX First Quarter 2024 Investor PresentationDBX First Quarter 2024 Investor Presentation
DBX First Quarter 2024 Investor Presentation
 
MINDCTI Revenue Release Quarter One 2024
MINDCTI Revenue Release Quarter One 2024MINDCTI Revenue Release Quarter One 2024
MINDCTI Revenue Release Quarter One 2024
 
Why Teams call analytics are critical to your entire business
Why Teams call analytics are critical to your entire businessWhy Teams call analytics are critical to your entire business
Why Teams call analytics are critical to your entire business
 
Apidays New York 2024 - The value of a flexible API Management solution for O...
Apidays New York 2024 - The value of a flexible API Management solution for O...Apidays New York 2024 - The value of a flexible API Management solution for O...
Apidays New York 2024 - The value of a flexible API Management solution for O...
 
[BuildWithAI] Introduction to Gemini.pdf
[BuildWithAI] Introduction to Gemini.pdf[BuildWithAI] Introduction to Gemini.pdf
[BuildWithAI] Introduction to Gemini.pdf
 
+971581248768>> SAFE AND ORIGINAL ABORTION PILLS FOR SALE IN DUBAI AND ABUDHA...
+971581248768>> SAFE AND ORIGINAL ABORTION PILLS FOR SALE IN DUBAI AND ABUDHA...+971581248768>> SAFE AND ORIGINAL ABORTION PILLS FOR SALE IN DUBAI AND ABUDHA...
+971581248768>> SAFE AND ORIGINAL ABORTION PILLS FOR SALE IN DUBAI AND ABUDHA...
 
Manulife - Insurer Transformation Award 2024
Manulife - Insurer Transformation Award 2024Manulife - Insurer Transformation Award 2024
Manulife - Insurer Transformation Award 2024
 
AWS Community Day CPH - Three problems of Terraform
AWS Community Day CPH - Three problems of TerraformAWS Community Day CPH - Three problems of Terraform
AWS Community Day CPH - Three problems of Terraform
 

TensorFlow 深度學習快速上手班--自然語言處理應用