AI4ALL: Natural Language Processing

Explore the recurrent neural network as the time-sequenced foundation for large-scale natural language tasks, with unfolded architectures, timestamped inputs, and hidden layers learning from past to future.

Explore the theoretical framework of recurrent neural networks for time series features, detailing how x_t passes through linear and nonlinear transforms to produce y_hat.

Learn how the embedding layer transforms text data from one-hot sparse vectors into dense word representations, reducing dimensionality while enabling backpropagation to learn pre-trained or random embeddings.

Explore the dropout technique in neural networks, dropping out a fraction of neurons during training to reduce overfitting, with per-layer application and a configurable percentage.

Explore how long short-term memory uses a cell state as a memory bank to preserve long-term information, updating with past state and current input through weighted transformations.

Train a sentiment analysis model for natural language processing on the IMDb movie review dataset, a balanced 50,000 review corpus with training and test splits labeled positive or negative.

Learn to convert IMDB movie reviews into numeric tokens using dictionary indices and padding, creating a uniform matrix for neural network processing.

Compare a simple recurrent neural network with an upcoming LSTM model by building a one-layer RNN in TensorFlow, using embeddings, and training a binary sentiment classifier with GPU support.

Improve IMDb sentiment analysis with a TensorFlow sequential model using embedding, dropout, and an LSTM layer to capture long-term dependencies and boost validation accuracy.

Evaluate IMDb sentiment predictions and analyze incorrect cases, exploring how model labels positive or negative statements and emotion cues in a live interactive playground for natural language processing.