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C2_W4_lecture_nb_1_data_prep.ipynb		C2_W4_lecture_nb_1_data_prep.ipynb
C2_W4_lecture_nb_2_intro_to_CBOW.ipynb		C2_W4_lecture_nb_2_intro_to_CBOW.ipynb
C2_W4_lecture_nb_3_training_the_CBOW.ipynb		C2_W4_lecture_nb_3_training_the_CBOW.ipynb
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C2_W4_lecture_nb_5_word_embeddings_step_by_step.ipynb		C2_W4_lecture_nb_5_word_embeddings_step_by_step.ipynb
README.md		README.md
utils2.py		utils2.py
w4_unittest.py		w4_unittest.py

README.md

Word embeddings with neural networks

Data Preparation

Complete this lecture notebook on data preparation techniques.
Intro to CBOW model

You will get an introduction to the CBOW model, its activation functions and practice numpy along the way.
Training the CBOW model

In this lecture notebook you will be doing some hands-on exercises on topics such as: Forward propagation, Cross-entropy loss, Backpropagation and Gradient descent.
Word Embeddings

In this lecture notebook you will see how to get word embedding vectors from a trained model.
Word embeddings step by step

Practicing on small examples will prepare you for the graded assignment, where you will combine the techniques in more advanced ways to create word embeddings from a real-life corpus.