Comments for Giuseppe Bonaccorso https://www.bonaccorso.eu Artificial Intelligence, Machine Learning, Deep Learning, Data Science, Philosophy of Mind Wed, 14 Feb 2018 17:01:14 +0000 hourly 1 https://wordpress.org/?v=4.9.4 Comment on A model-free collaborative recommendation system in 20 lines of Python code by Jayaraj Chanku https://www.bonaccorso.eu/2017/09/13/a-model-free-collaborative-recommendation-system-in-20-lines-of-python/#comment-75 Wed, 14 Feb 2018 17:01:14 +0000 https://www.bonaccorso.eu/?p=1325#comment-75 Great information. Thanks for sharing this amazing guidelines. This article really helped me to gather what I was searching for. Thank you once again.

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Comment on Twitter Sentiment Analysis with Gensim Word2Vec and Keras Convolutional Networks by Ayra https://www.bonaccorso.eu/2017/08/07/twitter-sentiment-analysis-with-gensim-word2vec-and-keras-convolutional-networks/#comment-74 Sun, 21 Jan 2018 23:05:11 +0000 https://www.bonaccorso.eu/?p=1080#comment-74 Thanks alot!I am trying to go through line by line to understand the code and i had my concepts build in terms of images so understanding in terms of 1D text is a bit new for me.So I have a few more questions since i am confused a bit:
1-As far as i can understand word2vec model is trained till like line 87,after that,the separation of training and test data is for CNN ,is my understanding right?

2-I wanted to run and see what exactly X_train looks like but i couldnt run it so i am assuming from dry run that its a matrix containing index,words and their corresponding vectors.If my understanding is right,then it means CNN takes 15 words as an input each time(which might or might not be the whole tweet) so when i make predictions how will it make sure that prediction is for one whole tweet?

3-I was thinking to use another dataset as well which is similar to one for which i want to make predictions for(e.g.phones) for training word2vec since it doesnt need labelled data and it will probably just increase dictionary.But i am concerned that CNN or CNN+LSTM wont be able to learn anything since i couldnt find any labelled dataset related to phones so if someone says there camera is 2MP vs someone who says 30MP,it wont be able to differentiate that the 2MP one is probably negative sentiment and 30 one is positive.Do you think that i should try to make predictions only if i have labelled dataset for that particular domain?

4-In LSTM timestamp according to me is how many previous steps you would want to consider before making next prediction,which ideally is all the words of one tweet(to see the whole context of the tweet) so in this case would it be 1?since CNN takes 15 words which is almost one tweet.Last_num_filters i think is based on feature map or filters that you have used in CNN so e.g. if in your code you did 8,would this be 8?

Sorry for really lengthy post and hope i make some sense atleast.
Thanks.

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Comment on Twitter Sentiment Analysis with Gensim Word2Vec and Keras Convolutional Networks by Giuseppe Bonaccorso https://www.bonaccorso.eu/2017/08/07/twitter-sentiment-analysis-with-gensim-word2vec-and-keras-convolutional-networks/#comment-73 Sun, 21 Jan 2018 10:22:21 +0000 https://www.bonaccorso.eu/?p=1080#comment-73 Hi,
1. The dataset is huge and you probably don’t have enough free memory. Try to reduce the train size. All my tests have been done with 32GB
2. A folder where you want to store the Gensim model so to avoid retraining every time
3. You should consider the words which are included in the production dataset. If they are very specific, it’s better to include a set of examples in the training set, or using a Word2Vec/GloVe/FastText pretrained model (there are many based on the whole Wikipedia corpus).
4. I’m still working on some improvements, however, in this case, the idea is to use the convolutions on the whole utterance (which is not considered like an actual sequence even if a Conv1D formally operates on a sequence), trying to discover the “geometrical” relationships that determine the semantics. You can easily try adding an LSTM layer before the dense layers (without flattening). In this case, the input will have a shape (batch_size, timesteps, last_num_filters). The LSTM output can be processed by one or more dense layers.

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Comment on Twitter Sentiment Analysis with Gensim Word2Vec and Keras Convolutional Networks by Ayra https://www.bonaccorso.eu/2017/08/07/twitter-sentiment-analysis-with-gensim-word2vec-and-keras-convolutional-networks/#comment-72 Sat, 20 Jan 2018 21:00:47 +0000 https://www.bonaccorso.eu/?p=1080#comment-72 Hi,i have few questions and since i am new to this they might be basic so sorry in advance.
1-I am getting “Memory error” on line 114,is it hardware issue or am i doing something wrong in code?
2-line number 33,what does it refer to?
3-If i train my model with this dataset and then want to predict for the dataset which are still tweets but related to some specific brand,would it still make sense in your opinion?
4-If i want to add LSTM (output from the CNN goes into LSTM for final classification),do you think it can improve results?If yes,can you guide a bit how to continue with your code to add that part?Thanks alot!

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Comment on Lossy image autoencoders with convolution and deconvolution networks in Tensorflow by Giuseppe Bonaccorso https://www.bonaccorso.eu/2017/07/29/lossy-image-autoencoders-convolution-deconvolution-networks-tensorflow/#comment-70 Tue, 16 Jan 2018 10:19:20 +0000 http://www.bonaccorso.eu/?p=1007#comment-70 Hi,
if you need to work with tensors like (batch_size, dim), you can simply use dense layers. A simple structure like Input -> Hidden 1 -> Code -> Hidden 2 -> Reconstruction can be enough. If the dataset has a temporal structure (batch_size, time step, dim), you can use 1D convolutions, however it depends on the specific problem.

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Comment on Lossy image autoencoders with convolution and deconvolution networks in Tensorflow by wine lover https://www.bonaccorso.eu/2017/07/29/lossy-image-autoencoders-convolution-deconvolution-networks-tensorflow/#comment-69 Mon, 15 Jan 2018 22:57:13 +0000 http://www.bonaccorso.eu/?p=1007#comment-69 Hi, Thanks for the post. If both input and output are of one-dimensional, how to modify your code to handle that kind of scenario?

Thanks,

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Comment on Hetero-Associative Memories for Non Experts: How “Stories” are memorized with Image-associations by Giuseppe Bonaccorso https://www.bonaccorso.eu/2017/12/31/hetero-associative-memories-non-experts-stories-memorized-image-associations/#comment-68 Fri, 05 Jan 2018 07:57:15 +0000 https://www.bonaccorso.eu/?p=1797#comment-68 There are many books (like the ones in the reference section. Theoretical Neuroscience is an excellent one, even if it’s not very recent) and several papers on arXiv. I suggest to follow their twitter accounts or search directly on the website.

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Comment on Hetero-Associative Memories for Non Experts: How “Stories” are memorized with Image-associations by tukabel https://www.bonaccorso.eu/2017/12/31/hetero-associative-memories-non-experts-stories-memorized-image-associations/#comment-67 Thu, 04 Jan 2018 22:02:32 +0000 https://www.bonaccorso.eu/?p=1797#comment-67 good job, time to progress in these high level functionalities – btw, is there any good neuroscience material on this topic (detailed research, not the usual “this area does that”?

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Comment on Hetero-Associative Memories for Non Experts: How “Stories” are memorized with Image-associations by Giuseppe Bonaccorso https://www.bonaccorso.eu/2017/12/31/hetero-associative-memories-non-experts-stories-memorized-image-associations/#comment-65 Mon, 01 Jan 2018 09:14:43 +0000 https://www.bonaccorso.eu/?p=1797#comment-65 Thank you Keghn!

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Comment on Hetero-Associative Memories for Non Experts: How “Stories” are memorized with Image-associations by keghn feem https://www.bonaccorso.eu/2017/12/31/hetero-associative-memories-non-experts-stories-memorized-image-associations/#comment-64 Sun, 31 Dec 2017 16:33:56 +0000 https://www.bonaccorso.eu/?p=1797#comment-64 Very interesting. I have been doing research in this area. Started from scratch and i called them
chaining RNN.
Your info will help me find more info. Thanks again.

Artificial general intelligence:
https://groups.google.com/forum/#!forum/artificial-general-intelligence

https://www.reddit.com/r/neuralnetworks/

https://www.reddit.com/r/deeplearning/

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