Lecture 2 added

Lecture 2 - PyTorch & Word2Vec/README.md

@@ -0,0 +1,18 @@
+# Лекция 2
+*Введение в PyTorch и реализация моделей Word2Vec*
+
+Ноутбук на Colab можно найти [здесь](https://colab.research.google.com/drive/15tg6jTt1F0oR5PzFlcZNBnpPiu4se1m3) 
+**Обратите внимание, ноутбук обновился в части про Word2Vec.**
+
+**Задание** 
+*Дедлайн: 09:00 GMT+3 15.03.18* 
+1. Реализовать Skip-Gram модель. 
+2. Реализовать Negative Sampling для CBoW модели. 
+3. (По желанию) Разобраться со статьёй [Two/Too Simple Adaptations of Word2Vec for Syntax Problems
+] (http:https://www.cs.cmu.edu/~lingwang/papers/naacl2015.pdf) и реализовать Structured Skip-gram Model модель.
+
+Дополнительные материалы: 
+[On word embeddings - Part 1, by Sebastian Ruder](http:https://ruder.io/word-embeddings-1/) 
+[On word embeddings - Part 2: Approximating the Softmax, by Sebastian Ruder](http:https://ruder.io/word-embeddings-softmax/index.html) 
+[cs224n "Lecture 2 | Word Vector Representations: word2vec" (video)](https://www.youtube.com/watch?v=ERibwqs9p38&index=2&list=PLqdrfNEc5QnuV9RwUAhoJcoQvu4Q46Lja&t=0s). 
+[cs224n "Lecture 5: Backpropagation and Project Advice" (video)](https://www.youtube.com/watch?v=isPiE-DBagM&index=5&list=PLqdrfNEc5QnuV9RwUAhoJcoQvu4Q46Lja&t=0s). 

README.md

@@ -6,4 +6,9 @@ The textbook: [Neural Network Methods in Natural Language Processing by Yoav Gol
 
 ## Lecture Materials
 ### Lecture 1
-A short overview of the most popular architectures in the deep learning. A brief introduction to the [keras](keras.io) framework.
+A short overview of the most popular architectures in the deep learning. A brief introduction into the [keras](keras.io) framework. Examples of models for sentiment analysis on the IMDB movie review dataset. 
+The [Colab notebook](https://drive.google.com/open?id=1KGy9Hm3y4asE6ohg3QD77w2nZV3V9_08).
+
+### Lecture 2
+An introduction into the [PyTorch](pytorch.org) framework. Examples of Word2Vec models. 
+The [Colab notebook](https://drive.google.com/open?id=1KGy9Hm3y4asE6ohg3QD77w2nZV3V9_08).