Personal Information
Organização/Local de trabalho
Bangalore India
Marcadores
machine learning
deep learning
natural language processing
deep neural networks
nlp
convolutional neural networks
image classification
neural networks
bayes theorem
probability theory
word representation
ai
computer vision
artificial intelligence
convolution
cnn
regression problems
linear models
classification problems
recurrent neural network
add 1 smoothing
word2vec
language model
joint probability distributions
cosine similarity
stemming
lemmatization
conditional gan
image to image translation
sagan
cyclegan
gan
generative adversarial networks
senet
inception
googlenet
localization
imagenet
resnet
backpropagation
adagrad
sigmoid
relu
variance
bias
underfitting
overfitting
gaussian naive bayes
generative models
independence
conditional independence
naive bayes classifier
product rule of probability
sum rule of probability
gaussian
random variables
bayes networks
graph search
state space
uniform cost search
a star
dfs
bfs
search problems
robotics
overview
introduction
latent semantic analysis
singular value decomposition
svd
lsa
neural turing machine
memory networks
speech recognition
speech synthesis
deep neural networks for speech
tensorflow
theano
deep learning frameworks
id3 algorithm
entropy
information gain
decision tree
supervised learning
perceptron learning
decision surface
unsupervised learning
gated recurrent units
gru
long short term memory
lstm
bidirectional rnn
rnn
tagging
maximum entropy models
ner
maxent
named entity recognition
loglinear
forward backward algorithm
backward algorithm
forward algorithm
hidden markov model
baum welch algorithm
hmm
viterbi algorithm
minimum edit distance
edit distance
applications of minimum edit distance
dynamic programming algorithm
interpolation
discounting
smoothing techniques
wordnet
distributed representation
n-grams
baysean networks
text normalization
words
tokenization
nlp applications
deep learning for practitioners
practical deep learning
feature learning
restricted boltzmann machines
autoencoders
introduction to deep learning
primer
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(27)Personal Information
Organização/Local de trabalho
Bangalore India
Marcadores
machine learning
deep learning
natural language processing
deep neural networks
nlp
convolutional neural networks
image classification
neural networks
bayes theorem
probability theory
word representation
ai
computer vision
artificial intelligence
convolution
cnn
regression problems
linear models
classification problems
recurrent neural network
add 1 smoothing
word2vec
language model
joint probability distributions
cosine similarity
stemming
lemmatization
conditional gan
image to image translation
sagan
cyclegan
gan
generative adversarial networks
senet
inception
googlenet
localization
imagenet
resnet
backpropagation
adagrad
sigmoid
relu
variance
bias
underfitting
overfitting
gaussian naive bayes
generative models
independence
conditional independence
naive bayes classifier
product rule of probability
sum rule of probability
gaussian
random variables
bayes networks
graph search
state space
uniform cost search
a star
dfs
bfs
search problems
robotics
overview
introduction
latent semantic analysis
singular value decomposition
svd
lsa
neural turing machine
memory networks
speech recognition
speech synthesis
deep neural networks for speech
tensorflow
theano
deep learning frameworks
id3 algorithm
entropy
information gain
decision tree
supervised learning
perceptron learning
decision surface
unsupervised learning
gated recurrent units
gru
long short term memory
lstm
bidirectional rnn
rnn
tagging
maximum entropy models
ner
maxent
named entity recognition
loglinear
forward backward algorithm
backward algorithm
forward algorithm
hidden markov model
baum welch algorithm
hmm
viterbi algorithm
minimum edit distance
edit distance
applications of minimum edit distance
dynamic programming algorithm
interpolation
discounting
smoothing techniques
wordnet
distributed representation
n-grams
baysean networks
text normalization
words
tokenization
nlp applications
deep learning for practitioners
practical deep learning
feature learning
restricted boltzmann machines
autoencoders
introduction to deep learning
primer
Ver mais