Comments for Giuseppe Bonaccorso https://www.bonaccorso.eu Artificial Intelligence, Machine Learning, Deep Learning, Data Science, Philosophy of Mind Wed, 16 May 2018 17:26:53 +0000 hourly 1 https://wordpress.org/?v=4.9.6 Comment on SVD Recommendations using Tensorflow by Giuseppe Bonaccorso https://www.bonaccorso.eu/2017/08/02/svd-recommendations-using-tensorflow/#comment-88 Wed, 16 May 2018 17:26:53 +0000 https://www.bonaccorso.eu/?p=1039#comment-88 Hi,
thanks! In this case, the training process is done through the SVD. An alternative approach which is based on a cost function (mean squared error) and a training operator is the Non-Negative Matrix Factorization. I’m going to add this example in a future post.

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Comment on SVD Recommendations using Tensorflow by Yongguang Zhang https://www.bonaccorso.eu/2017/08/02/svd-recommendations-using-tensorflow/#comment-87 Wed, 16 May 2018 09:06:22 +0000 https://www.bonaccorso.eu/?p=1039#comment-87 That’ what I need! thx!
BTW, I think it’s will look better if u wrote the code of training process.
I mean cost and train_op.
It’s make it easier to understand for the beginer.
Thanks again.

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Comment on ML Algorithms Addendum: Hopfield Networks by Giuseppe Bonaccorso https://www.bonaccorso.eu/2017/09/20/ml-algorithms-addendum-hopfield-networks/#comment-86 Thu, 26 Apr 2018 07:03:50 +0000 https://www.bonaccorso.eu/?p=1376#comment-86 Thanks!

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Comment on ML Algorithms Addendum: Hopfield Networks by Mence https://www.bonaccorso.eu/2017/09/20/ml-algorithms-addendum-hopfield-networks/#comment-85 Wed, 25 Apr 2018 23:39:24 +0000 https://www.bonaccorso.eu/?p=1376#comment-85 an awesome stuff! thank you for posting!

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Comment on An annotated path to start with Machine Learning by Giuseppe Bonaccorso https://www.bonaccorso.eu/2017/09/09/an-annotated-path-to-start-with-machine-learning/#comment-84 Tue, 10 Apr 2018 11:13:19 +0000 https://www.bonaccorso.eu/?p=1292#comment-84 Thank for your comment! I’ve just updated a few broken links (nothing changed since the original version, but I’m going to add new resources in a next post).

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Comment on An annotated path to start with Machine Learning by Mario https://www.bonaccorso.eu/2017/09/09/an-annotated-path-to-start-with-machine-learning/#comment-83 Mon, 09 Apr 2018 22:29:21 +0000 https://www.bonaccorso.eu/?p=1292#comment-83 Hello Giuseppe – and thanks for the invaluable resource.

I notice some text recommendations are striken off – is that because you don’t recommend those books anymore?
Regards
Mario

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Comment on Twitter Sentiment Analysis with Gensim Word2Vec and Keras Convolutional Networks by Ahmed https://www.bonaccorso.eu/2017/08/07/twitter-sentiment-analysis-with-gensim-word2vec-and-keras-convolutional-networks/#comment-82 Sun, 25 Mar 2018 07:44:25 +0000 https://www.bonaccorso.eu/?p=1080#comment-82 Hi .. it’s worked with 100.000sample but very slow .. I have another question .. how can I fed a new review to get it’s sentiment predict ?

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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-81 Sun, 25 Mar 2018 07:41:48 +0000 https://www.bonaccorso.eu/?p=1080#comment-81 Hi,
unfortunately, I can’t help you. You don’t enough free memory. Try to reset the notebook (if using Jupyter) after reducing the number of samples. You can also reduce the max_tweet_length and the vector size. Consider that I worked with 32 GB but many people successfully trained the model with 16 GB.

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Comment on Twitter Sentiment Analysis with Gensim Word2Vec and Keras Convolutional Networks by ahmed https://www.bonaccorso.eu/2017/08/07/twitter-sentiment-analysis-with-gensim-word2vec-and-keras-convolutional-networks/#comment-80 Fri, 23 Mar 2018 15:32:59 +0000 https://www.bonaccorso.eu/?p=1080#comment-80 error in line 116
MemoryError Traceback (most recent call last)
in ()
2 indexes = set(np.random.choice(len(tokenized_corpus), train_size + test_size, replace=False))
3
—-> 4 X_train = np.zeros((train_size, max_tweet_length, vector_size), dtype=K.floatx())
5 Y_train = np.zeros((train_size, 2), dtype=np.int32)
6 X_test = np.zeros((test_size, max_tweet_length, vector_size), dtype=K.floatx())

MemoryError:
please hellp
i tried to reduce trsining and test data to 750000 or even 100000 and didnot work

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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-79 Mon, 19 Mar 2018 16:49:20 +0000 https://www.bonaccorso.eu/?p=1080#comment-79 Hi,
thanks a lot for your comment! Of course, you can work with new tweets. What you should do is similar to this part:

for i, index in enumerate(indexes):
for t, token in enumerate(tokenized_corpus[index]):
if t >= max_tweet_length:
break

    if token not in X_vecs:
        continue

    if i < train_size:
        X_train[i, t, :] = X_vecs[token]
    else:
        X_test[i - train_size, t, :] = X_vecs[token]

...

In other words, you need first to tokenize the tweet, then lookup for the word vectors corresponding to each token.
However, I’m planning to post a new article based on FastText and I’m going to add a specific section for querying the model.

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