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Results tagged with deep-learning
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user 2844
For questions related to deep learning, which refers to a subset of machine learning methods based on artificial neural networks (ANNs) with multiple hidden layers. The adjective deep thus refers to the number of layers of the ANNs. The expression deep learning was apparently introduced (although not in the context of machine learning or ANNs) in 1986 by Rina Dechter in the paper "Learning while searching in constraint-satisfaction-problems".
2
votes
How does YOLO handle non-class objects?
The output of YOLO is (x,y,w,h,confidence,class). The confidence value presents whether the rectangle holds an object, the rectangle is non-classed when confidence is low.
The class value will be used …
- 1,318
1
vote
Is feature engineer an important step for a deep learning approach?
From what I believe, feature engineering is important, it's a part of the job of ML network designer.
Network designing involves
Feature engineering: What should be in the input to the network, as pr …
- 1,318
0
votes
How to calculate the GPU memory need to run a deep learning network?
TensorFlow is interesting that it can store not only weights, but also training data in video RAM.
with tf.device('/gpu:0'):
tensorflow_dataset = tf.constant(numpy_dataset)
Feeding training data …
- 1,318
2
votes
1
answer
1k
views
How to make DNN learn multiplication/division?
A single neuron with 2 weights and identity activation can learn addition/subtraction as the 2 weights will converge to 1 and 1 (addition), or 1 and -1 (subtraction).
However, for multiplication and …
- 1,318
12
votes
1
answer
3k
views
What are all the different kinds of neural networks used for? [closed]
I found the following neural network cheat sheet (Cheat Sheets for AI, Neural Networks, Machine Learning, Deep Learning & Big Data).
What are all these different kinds of neural networks used for? …
- 1,318
0
votes
Accepted
Is it still called linear separation with a layer of more than 1 neuron
I found out the curvy zigzag green line is not polynomial as if it were polynomial, a vertical line won't cut that curvy line more than 1 time.
It's the combination of straight lines (linear separat …
- 1,318
0
votes
Accepted
How to map X to Y for TensorFlow RNN training data
I found out how to get 3D output from LSTMCell so that I can matmul with output weights + biases and subtract with expected values:
Inputs & expecteds should be: placeholder(,[times,batch_size,num_i …
- 1,318
0
votes
LSTM network doesn't converge, what should be changed?
I'm still working on how to make the code work for text generation, but the following converges and work for text classification:
import tensorflow as tf;
tf.reset_default_graph();
#data
'''
t0 …
- 1,318
1
vote
Why is embedding important in NLP, and how does autoencoder work?
The subword-based embedding is rather visual and easily understandable. However, the autoencoder embedding is what machines understand the componential meaning of words.
1) An autoencoder embedding l …
- 1,318
0
votes
1
answer
56
views
Solution to classify product names
I have a bunch of training data for classifying product names, around 30,000 samples. The task is to classify these product names into types of product, around 100 classes (single words).
For example …
- 1,318
0
votes
1
answer
82
views
How to map X to Y for TensorFlow RNN training data
Usually for DNN, I have the training data of matching X (2D) to Y (2D), for example, XOR data:
X = [[0,0],[0,1],[1,0],[1,1]];
Y = [[0], [1], [1], [0] ];
However, RNN seems strange, I don't get …
- 1,318
0
votes
2
answers
2k
views
LSTM network doesn't converge, what should be changed? [closed]
I'm testing out TensorFlow LSTM layer text generation task, not classification task; but something is wrong with my code, it doesn't converge. What changes should be done?
Source code:
import tensor …
- 1,318
1
vote
2
answers
421
views
Why is embedding important in NLP, and how does autoencoder work?
People say embedding is necessary in NLP because if using just the word indices, the efficiency is not high as similar words are supposed to be related to each other. However, I still don't truly get …
- 1,318
3
votes
2
answers
746
views
How to use LSTM to generate a paragraph
A LSTM model can be trained to generate text sequences by feeding the first word. After feeding the first word, the model will generate a sequence of words (a sentence). Feed the first word to get the …
- 1,318
1
vote
1
answer
124
views
What should the output of a neural network that needs to classify in an unsupervised fashion...
XOR data, without labels:
[[0,0],[0,1],[1,0],[1,1]]
I'm using this network for auto-classifying XOR data:
H1 <-- Dense(units=2, activation=relu) #any activation here
Z <-- Dense(units=2, activa …
- 1,318