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
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
DONE
DONE
DONE (best performance with the NotMNIST dataset: 96.1%)
DONE (CBOW model in word2vec)
- 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/