Word-Representation-and-Text-Classification-with-Neural-Networks

This project is divided into four parts. For all parts A to D, here is the breakdown of the tasks...

Part A: Word Embeddings with Word2Vec

1. Pre-processing the training corpus
2. Creating the corpus vocabulary and preparing the dataset
3. Building a skip-gram neural network architecture
4. Training the models
5. Getting the Word Embeddings
6. Exploring and visualizing your word embeddings using t-SNE

Part B: Basic Text Classification

1. Developed a neural network classifier using one-hot word vectors, trained and evaluated
2. Modify model to use a word embedding layer instead of one-hot vectors, and to learn the values of these word embedding vectors along with the model Model Summary
3. Adapt model to load and use pre-trained word embeddings in- stead and train and evaluate it
4. Improve the performance by adding another fully-connected layer to your network.

Part C: Using LSTMs for Text Classification

1. Readying the inputs for the LSTM
2. Building the model
3. Plot the Training & Validation Accuracy
4. Evaluating the model on the test data
5. Extracting the word embeddings
6. Visualizing the reviews
7. Visualizing the word embeddings

Part D: A Real Text Classification Task