Unlock the Power of Text with TensorFlow
Natural Language Processing (NLP) is a fascinating field that enables computers to understand, interpret, and generate human language. TensorFlow, a leading open-source library for machine learning, provides a robust ecosystem for building and deploying sophisticated NLP models. This collection of tutorials will guide you through various NLP tasks using TensorFlow, from basic text processing to advanced deep learning architectures.
Whether you're a beginner looking to get started or an experienced practitioner seeking to deepen your knowledge, these resources offer practical examples and in-depth explanations.
Featured Tutorials
Sentiment Analysis with RNNs
Learn to build a recurrent neural network (RNN) model to classify the sentiment of text data, like movie reviews.
import tensorflow as tf
from tensorflow.keras.layers import Embedding, SimpleRNN, Dense
# ... model definition ...
model = tf.keras.Sequential([
Embedding(input_dim=vocab_size, output_dim=embedding_dim),
SimpleRNN(units=32),
Dense(1, activation='sigmoid')
])
# ... training code ...
Machine Translation with LSTMs
Develop a sequence-to-sequence model using LSTMs to translate text from one language to another.
# Encoder-Decoder Architecture
encoder_input = tf.keras.Input(shape=(None,))
decoder_target = tf.keras.Input(shape=(None,))
encoder_lstm = tf.keras.layers.LSTM(latent_dim, return_state=True)
encoder_outputs, state_h, state_c = encoder_lstm(encoder_embedding)
decoder_lstm = tf.keras.layers.LSTM(latent_dim, return_sequences=True, return_state=True)
decoder_outputs, _, _ = decoder_lstm(decoder_embedding, initial_state=[state_h, state_c])
# ... output layer ...
Character-Level Text Generation
Explore how to generate new text that mimics a given style and content using character-level prediction.
model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
# Example generation snippet
def sample(preds, temperature=1.0):
preds = np.asarray(preds).astype('float64')
preds = np.log(preds + 1e-7) / temperature
exp_preds = np.exp(preds)
preds = exp_preds / np.sum(exp_preds)
probas = np.random.multinomial(1, preds, 1)
return np.argmax(probas)
Understanding Word Embeddings
Dive into the concepts of word embeddings like Word2Vec and GloVe, and how to use them in TensorFlow.
from tensorflow.keras.layers import Embedding
from gensim.models import Word2Vec
# Load pre-trained embeddings or train your own
model = Word2Vec(sentences=tokenized_sentences, vector_size=100, window=5, min_count=5, workers=4)
embedding_matrix = model.wv.get_matrix()
embedding_layer = Embedding(input_dim=vocab_size,
output_dim=embedding_dim,
weights=[embedding_matrix],
trainable=False)