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udacity-deep_learning

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Jupyter notebooks (with answers) used during the Deep Learning MOOC

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# udacity-deep_learning
Jupyter notebooks (with answers) used during the Google Deep Learning MOOC

The goal of this MOOC is to complete all the assignments in the "tensorflow/examples/udacity" directory of tensorflow

MOOC url: https://www.udacity.com/course/deep-learning--ud730

## Status: DONE!

### Assigment 1: logistic regression
nearly DONE
* I have not created the sanitized dataset. (Anyway the results seem good with messy data)

*BONUS:*
* Comparison of logistic regression results with 2 other machine learning algorithms from scikit learn: gaussian Naive Bayes and random forest regressor
 
### Assigment 2: basic neural network
DONE

### Assigment 3: regularization
DONE

### Assigment 4: convolutions
DONE (best performance with the NotMNIST dataset: 96.1%)

### Assigment 5: word2vec
DONE (CBOW model in word2vec)

### Assigment 6: recurrent neural networks with LSTM (and GRU) cells
* Problem 1: DONE
* Problem 2: DONE
* Problem 3: DONE (sequence-to-sequence models)

*BONUS for problem 2:*
* implement a GRU cell instead of an LSTM cell
* mix LSTM and GRU cells at the same time
* the GRU equations are taken from this great blog post on RNNs: http://colah.github.io/posts/2015-08-Understanding-LSTMs/

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